{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T05:46:04.098789Z",
     "start_time": "2018-10-28T05:46:01.362583Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa import stattools\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "import numpy\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import csv\n",
    "import os\n",
    "from bs4 import BeautifulSoup\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "def get_load_data(date):\n",
    "    url = 'http://www.delhisldc.org/Loaddata.aspx?mode='\n",
    "    print('Scraping', date)\n",
    "    resp = requests.get(url+date) # send a get request to the url, get response\n",
    "    soup = BeautifulSoup(resp.text, 'lxml') # Yummy HTML soup\n",
    "    table = soup.find('table', {'id':'ContentPlaceHolder3_DGGridAv'}) # get the table from html\n",
    "    trs = table.findAll('tr') # extract all rows of the table\n",
    "    if len(trs[1:])!=0: # no need to create csv file, if there's no data, for Aug month of 2017\n",
    "        csv_filename = 'monthdata.csv'\n",
    "        with open(csv_filename, 'a') as f:\n",
    "            writer = csv.writer(f)\n",
    "            count=0\n",
    "            for tr in trs[1:]:\n",
    "                time, delhi = tr.findChildren('font')[:2]\n",
    "                writer.writerow([date+' '+time.text, delhi.text])\n",
    "                count+=1\n",
    "    if count != 288:\n",
    "        print('Some of the load values are missing..')\n",
    "    else:\n",
    "        print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2018-10-27'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# date = date.split('/')\n",
    "# date.reverse()\n",
    "'-'.join(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scraping 27/09/2018\n",
      "Done\n",
      "Scraping 28/09/2018\n",
      "Done\n",
      "Scraping 29/09/2018\n",
      "Done\n",
      "Scraping 30/09/2018\n",
      "Done\n",
      "Scraping 01/10/2018\n",
      "Done\n",
      "Scraping 02/10/2018\n",
      "Done\n",
      "Scraping 03/10/2018\n",
      "Done\n",
      "Scraping 04/10/2018\n",
      "Done\n",
      "Scraping 05/10/2018\n",
      "Done\n",
      "Scraping 06/10/2018\n",
      "Done\n",
      "Scraping 07/10/2018\n",
      "Done\n",
      "Scraping 08/10/2018\n",
      "Done\n",
      "Scraping 09/10/2018\n",
      "Done\n",
      "Scraping 10/10/2018\n",
      "Done\n",
      "Scraping 11/10/2018\n",
      "Done\n",
      "Scraping 12/10/2018\n",
      "Done\n",
      "Scraping 13/10/2018\n",
      "Done\n",
      "Scraping 14/10/2018\n",
      "Done\n",
      "Scraping 15/10/2018\n",
      "Done\n",
      "Scraping 16/10/2018\n",
      "Done\n",
      "Scraping 17/10/2018\n",
      "Done\n",
      "Scraping 18/10/2018\n",
      "Done\n",
      "Scraping 19/10/2018\n",
      "Done\n",
      "Scraping 20/10/2018\n",
      "Done\n",
      "Scraping 21/10/2018\n",
      "Done\n",
      "Scraping 22/10/2018\n",
      "Done\n",
      "Scraping 23/10/2018\n",
      "Some of the load values are missing..\n",
      "Scraping 24/10/2018\n",
      "Done\n",
      "Scraping 25/10/2018\n",
      "Done\n",
      "Scraping 26/10/2018\n",
      "Done\n",
      "Scraping 27/10/2018\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "for i in range(31, 0, -1):\n",
    "    yesterday = datetime.today() - timedelta(i)\n",
    "    yesterday = yesterday.strftime('%d/%m/%Y')\n",
    "    get_load_data(yesterday)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scraping 27/10/2018\n",
      "Done\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/09/2018 00:00,3426.820\n",
      "27/09/2018 00:05,3398.530\n",
      "27/09/2018 00:10,3376.950\n",
      "27/09/2018 00:15,3356.010\n",
      "27/09/2018 00:20,3345.170\n",
      "27/09/2018 00:25,3313.630\n",
      "27/09/2018 00:30,3314.760\n",
      "27/09/2018 00:35,3303.790\n",
      "27/09/2018 00:40,3289.300\n",
      "27/09/2018 00:45,3277.190\n"
     ]
    }
   ],
   "source": [
    "!head monthdata.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/12/2017,00:00,1793.03\n",
      "24/12/2017,00:05,1781.27\n",
      "24/12/2017,00:10,1757.1\n",
      "24/12/2017,00:15,1730.53\n",
      "24/12/2017,00:20,1716.35\n",
      "24/12/2017,00:25,1697.04\n",
      "24/12/2017,00:30,1686.24\n",
      "24/12/2017,00:35,1678.91\n",
      "24/12/2017,00:40,1654.96\n",
      "24/12/2017,00:45,1638.91\n"
     ]
    }
   ],
   "source": [
    "!head delhi.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('monthdata.csv', skiprows=[0], header=None, names=['datetime', 'load'], index_col=[0], parse_dates=[0], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 8946 entries, 28/09/2018 00:00 to 28/10/2018 23:55\n",
      "Data columns (total 1 columns):\n",
      "load    8946 non-null object\n",
      "dtypes: object(1)\n",
      "memory usage: 459.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8946, 1)"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'26/10/2018'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   4240\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4241\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_searchsorted_monotonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4242\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_searchsorted_monotonic\u001b[0;34m(self, label, side)\u001b[0m\n\u001b[1;32m   4199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4200\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'index must be monotonic increasing or decreasing'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: index must be monotonic increasing or decreasing",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-292-f4141b4b8169>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'26/10/2018'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2675\u001b[0m         \u001b[0;31m# see if we can slice the rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2676\u001b[0;31m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_index_sliceable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2677\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2678\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36mconvert_to_index_sliceable\u001b[0;34m(obj, key)\u001b[0m\n\u001b[1;32m   2324\u001b[0m     \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2325\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2326\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_slice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'getitem'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2328\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_convert_slice_indexer\u001b[0;34m(self, key, kind)\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1750\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1751\u001b[0;31m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1752\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1753\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mis_index_slice\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   4105\u001b[0m         \"\"\"\n\u001b[1;32m   4106\u001b[0m         start_slice, end_slice = self.slice_locs(start, end, step=step,\n\u001b[0;32m-> 4107\u001b[0;31m                                                  kind=kind)\n\u001b[0m\u001b[1;32m   4108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4109\u001b[0m         \u001b[0;31m# return a slice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   4306\u001b[0m         \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4307\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4308\u001b[0;31m             \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'left'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4309\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4310\u001b[0m             \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   4242\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4243\u001b[0m                 \u001b[0;31m# raise the original KeyError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4244\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4246\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   4236\u001b[0m         \u001b[0;31m# we need to look up the label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4237\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4238\u001b[0;31m             \u001b[0mslc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_loc_only_exact_matches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4239\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4240\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_get_loc_only_exact_matches\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4205\u001b[0m         \u001b[0mget_slice_bound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4206\u001b[0m         \"\"\"\n\u001b[0;32m-> 4207\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4209\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3078\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3082\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._get_loc_duplicates\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._maybe_get_bool_indexer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '26/10/2018'"
     ]
    }
   ],
   "source": [
    "data['26/10/2018':].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:58:46.525724Z",
     "start_time": "2018-10-28T07:58:45.741543Z"
    }
   },
   "outputs": [],
   "source": [
    "delhi = pd.read_csv('delhi.csv', header = None, parse_dates=[[0,1]], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:58:46.566459Z",
     "start_time": "2018-10-28T07:58:46.536946Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>2017-12-24 00:00:00</td>\n",
       "      <td>1793.03</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-24 00:05:00</td>\n",
       "      <td>1781.27</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-24 00:10:00</td>\n",
       "      <td>1757.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-24 00:15:00</td>\n",
       "      <td>1730.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-24 00:20:00</td>\n",
       "      <td>1716.35</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0_1        2\n",
       "0 2017-12-24 00:00:00  1793.03\n",
       "1 2017-12-24 00:05:00  1781.27\n",
       "2 2017-12-24 00:10:00  1757.10\n",
       "3 2017-12-24 00:15:00  1730.53\n",
       "4 2017-12-24 00:20:00  1716.35"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:58:46.579365Z",
     "start_time": "2018-10-28T07:58:46.571440Z"
    }
   },
   "outputs": [],
   "source": [
    "delhi.columns = ['datetime', 'load']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:58:46.949451Z",
     "start_time": "2018-10-28T07:58:46.942747Z"
    }
   },
   "outputs": [],
   "source": [
    "delhi.index = delhi['datetime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:58:47.219078Z",
     "start_time": "2018-10-28T07:58:47.210050Z"
    }
   },
   "outputs": [],
   "source": [
    "delhi = delhi.drop(columns = ['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:58:49.378657Z",
     "start_time": "2018-10-28T07:58:49.354137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-24 00:00:00</th>\n",
       "      <td>1793.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24 00:05:00</th>\n",
       "      <td>1781.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24 00:10:00</th>\n",
       "      <td>1757.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24 00:15:00</th>\n",
       "      <td>1730.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24 00:20:00</th>\n",
       "      <td>1716.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2017-12-24 00:00:00  1793.03\n",
       "2017-12-24 00:05:00  1781.27\n",
       "2017-12-24 00:10:00  1757.10\n",
       "2017-12-24 00:15:00  1730.53\n",
       "2017-12-24 00:20:00  1716.35"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:34:14.132016Z",
     "start_time": "2018-10-28T06:34:14.118651Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69036, 1)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delhi = delhi.apply(lambda x : (x-x.mean())/(x.max()-x.min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (delhi[delhi.index.minute==30] or [delhi.index.minute==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27/09/2018 00:00</th>\n",
       "      <td>3426.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "datetime                 \n",
       "27/09/2018 00:00  3426.82"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['27/09/2018'].plot()\n",
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:05:38.960118Z",
     "start_time": "2018-10-28T06:05:38.431837Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delhi['2018-08-07'].plot()\n",
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# r = delhi.rolling(window = 12*24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delhi['load'].plot(color = 'blue')\n",
    "# r.mean()['load'].plot(color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delhi['load'].plot(color = 'blue')\n",
    "# r.max()['load'].plot(color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delhi['load'].expanding(min_periods = 50).mean().plot()\n",
    "# data['electricity_consumption'].expanding(min_periods = 720).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:05:42.820146Z",
     "start_time": "2018-10-28T06:05:42.792459Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-21 23:35:00</th>\n",
       "      <td>5802.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 23:40:00</th>\n",
       "      <td>5772.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 23:45:00</th>\n",
       "      <td>5740.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 23:50:00</th>\n",
       "      <td>5733.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 23:55:00</th>\n",
       "      <td>5713.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-08-21 23:35:00  5802.66\n",
       "2018-08-21 23:40:00  5772.58\n",
       "2018-08-21 23:45:00  5740.85\n",
       "2018-08-21 23:50:00  5733.31\n",
       "2018-08-21 23:55:00  5713.83"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(delhi['load'][0:12*24])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(stattools.acf(delhi['load'], nlags = 12*24*3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(stattools.pacf(delhi['load'], nlags = 12*24*4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:59:00.899113Z",
     "start_time": "2018-10-28T07:59:00.868404Z"
    }
   },
   "outputs": [],
   "source": [
    "data = data.asfreq(freq='H', method='bfill')\n",
    "delhi = delhi.asfreq(freq='H', method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:59:01.250212Z",
     "start_time": "2018-10-28T07:59:01.241582Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5784, 1)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:36:19.849354Z",
     "start_time": "2018-10-28T06:36:19.837391Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "241"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique dates in the data\n",
    "len(set(delhi.index.date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:38:29.154862Z",
     "start_time": "2018-10-28T06:38:29.142964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-21 19:00:00</th>\n",
       "      <td>5031.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 20:00:00</th>\n",
       "      <td>5263.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 21:00:00</th>\n",
       "      <td>5245.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 22:00:00</th>\n",
       "      <td>5629.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 23:00:00</th>\n",
       "      <td>5850.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-08-21 19:00:00  5031.97\n",
       "2018-08-21 20:00:00  5263.41\n",
       "2018-08-21 21:00:00  5245.19\n",
       "2018-08-21 22:00:00  5629.48\n",
       "2018-08-21 23:00:00  5850.43"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:38:52.578564Z",
     "start_time": "2018-10-28T06:38:52.571961Z"
    }
   },
   "outputs": [],
   "source": [
    "delhi = delhi['2018-07-22':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:38:53.291919Z",
     "start_time": "2018-10-28T06:38:53.283589Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(delhi.index.date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import itertools\n",
    "# p=d=q =  range(0,2)\n",
    "# pdq = list(itertools.product(p,d,q))\n",
    "# print(pdq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p,d,q))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delhi.index = pd.to_datetime(delhi.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:05:51.465495Z",
     "start_time": "2018-10-28T06:05:51.455719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "decompfreq = 24 #daily freq\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "result = seasonal_decompose(data['10/2018':], freq=decompfreq, model='aditive')\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:05:55.052489Z",
     "start_time": "2018-10-28T06:05:53.948776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "decompfreq = 24 #daily freq\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "result = seasonal_decompose(delhi['2018-08':], freq=decompfreq, model='aditive')\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T05:50:37.726630Z",
     "start_time": "2018-10-28T05:50:36.548261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "decompfreq = 48 #daily freq\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "result = seasonal_decompose(delhi['2018-08':], freq=decompfreq, model='multiplicitive')\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:28:15.252972Z",
     "start_time": "2018-10-28T06:28:12.062286Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:30:29.917277Z",
     "start_time": "2018-10-28T06:30:29.889437Z"
    }
   },
   "outputs": [],
   "source": [
    "# sorted(text.split('\\n'), key=lambda x: x.split('-')[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import warnings\n",
    "# import statsmodels.api as sm\n",
    "# warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n",
    "\n",
    "# for param in pdq:\n",
    "#     for param_seasonal in seasonal_pdq:\n",
    "#         try:\n",
    "#             mod = sm.tsa.statespace.SARIMAX(delhi,\n",
    "#                                             order=param,\n",
    "#                                             seasonal_order=param_seasonal,\n",
    "#                                             enforce_stationarity=False,\n",
    "#                                             enforce_invertibility=False)\n",
    "\n",
    "#             results = mod.fit()\n",
    "\n",
    "#             print('ARIMA{}x{}12 - AIC:{}'.format(param, param_seasonal, results.aic))\n",
    "#         except:\n",
    "#             print(\"nhi\")\n",
    "#             continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pyramid.arima'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-420-d1643036edba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpyramid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marima\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mauto_arima\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m stepwise_model = auto_arima(delhi, start_p=1, start_q=1,\n\u001b[1;32m      3\u001b[0m                            \u001b[0mmax_p\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_q\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                            \u001b[0mstart_P\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseasonal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                            \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyramid.arima'"
     ]
    }
   ],
   "source": [
    "from pyramid.arima import auto_arima\n",
    "stepwise_model = auto_arima(delhi, start_p=1, start_q=1,\n",
    "                           max_p=3, max_q=3, m=12,\n",
    "                           start_P=0, seasonal=True,\n",
    "                           d=1, D=1, trace=True,\n",
    "                           error_action='ignore',  \n",
    "                           suppress_warnings=True, \n",
    "                           stepwise=True)\n",
    "print(stepwise_model.aic())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyramid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'pyramid' from '/home/eee/ug/15084015/miniconda3/envs/ML/lib/python3.7/site-packages/pyramid/__init__.py'>"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:33:17.857650Z",
     "start_time": "2018-10-28T06:33:17.851143Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13 µs, sys: 6 µs, total: 19 µs\n",
      "Wall time: 40.3 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(69036, 1)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "delhi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:45:37.843229Z",
     "start_time": "2018-10-28T06:39:08.590211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10 µs, sys: 5 µs, total: 15 µs\n",
      "Wall time: 40.3 µs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eee/ug/15084015/miniconda3/envs/ML/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mod = sm.tsa.statespace.SARIMAX(data,\n",
    "                                order=(0,1,1),\n",
    "                                seasonal_order=(0,2,2,24),\n",
    "                                enforce_stationarity=False,\n",
    "                                enforce_invertibility=False)\n",
    "\n",
    "results = mod.fit()\n",
    "print(results.summary().tables[1])\n",
    "'''For fitting on 30days of hourly data this takes , and 6500% CPU'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:52:06.177576Z",
     "start_time": "2018-10-28T07:52:06.124574Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                <td>load</td>              <th>  No. Observations:  </th>    <td>744</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 1)x(0, 2, 2, 24)</td> <th>  Log Likelihood     </th> <td>-3891.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Sun, 28 Oct 2018</td>        <th>  AIC                </th> <td>7791.532</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>13:22:06</td>            <th>  BIC                </th> <td>7809.409</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                    <td>07-22-2018</td>           <th>  HQIC               </th> <td>7798.469</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>- 08-21-2018</td>          <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>    <td>    0.3580</td> <td>    0.027</td> <td>   13.505</td> <td> 0.000</td> <td>    0.306</td> <td>    0.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L24</th> <td>   -1.9868</td> <td>    0.039</td> <td>  -50.818</td> <td> 0.000</td> <td>   -2.063</td> <td>   -1.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L48</th> <td>    1.0000</td> <td>    0.045</td> <td>   22.061</td> <td> 0.000</td> <td>    0.911</td> <td>    1.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 7917.3596</td> <td> 1.06e-05</td> <td> 7.49e+08</td> <td> 0.000</td> <td> 7917.360</td> <td> 7917.360</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>61.58</td> <th>  Jarque-Bera (JB):  </th> <td>373.36</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.02</td>  <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.38</td>  <th>  Skew:              </th>  <td>-0.53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.02</td>  <th>  Kurtosis:          </th>  <td>6.58</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 4.34e+23. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 Statespace Model Results                                 \n",
       "==========================================================================================\n",
       "Dep. Variable:                               load   No. Observations:                  744\n",
       "Model:             SARIMAX(0, 1, 1)x(0, 2, 2, 24)   Log Likelihood               -3891.766\n",
       "Date:                            Sun, 28 Oct 2018   AIC                           7791.532\n",
       "Time:                                    13:22:06   BIC                           7809.409\n",
       "Sample:                                07-22-2018   HQIC                          7798.469\n",
       "                                     - 08-21-2018                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1          0.3580      0.027     13.505      0.000       0.306       0.410\n",
       "ma.S.L24      -1.9868      0.039    -50.818      0.000      -2.063      -1.910\n",
       "ma.S.L48       1.0000      0.045     22.061      0.000       0.911       1.089\n",
       "sigma2      7917.3596   1.06e-05   7.49e+08      0.000    7917.360    7917.360\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       61.58   Jarque-Bera (JB):               373.36\n",
       "Prob(Q):                              0.02   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.38   Skew:                            -0.53\n",
       "Prob(H) (two-sided):                  0.02   Kurtosis:                         6.58\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 4.34e+23. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:06:06.369019Z",
     "start_time": "2018-10-28T06:06:06.363503Z"
    }
   },
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMAResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:06:06.703825Z",
     "start_time": "2018-10-28T06:06:06.694353Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.9.0'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels\n",
    "statsmodels.__version__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:56:32.083591Z",
     "start_time": "2018-10-28T07:56:29.353117Z"
    }
   },
   "outputs": [],
   "source": [
    "results.save('model1.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:57:05.826783Z",
     "start_time": "2018-10-28T07:57:04.171658Z"
    }
   },
   "outputs": [],
   "source": [
    "loaded = ARIMAResults.load('ARIMA_month_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:57:09.098846Z",
     "start_time": "2018-10-28T07:57:06.569880Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loaded.plot_diagnostics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:57:17.653867Z",
     "start_time": "2018-10-28T07:57:15.298523Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9evRollWTFT2GYRiGYRiGYTKK9qzkSZp7DljRYxiGYRiGYRiGaWOwoscwDMMwDMMwDGOgsLBQ//zVV1/hqKOOwu7duzF+/HgMGDAAI0eOxNChQ3HllVdiw4YNetszzzwTRx99NEaOHImRI0fiqquuckN8AJyMhWEYhmEYhmEYxpLZs2fjnnvuwTfffIPBgwcDAO69917cf//9AICPPvoIZ599NtauXYtevXoBAN577z2ceOKJrsksYYsewzAMwzAMwzCMiblz5+K2227Dl19+iSOOOMKyzS9/+Uv87Gc/w/vvv59m6eLDFj2XqG4M4t6PVmHCL45H7075bovDMAzDMAyT3dSVAnmdgBweV7Upvn4AOLi2Zfvsezxw4QTHJn6/H5dffjnmzJmDY445xrHtqFGjsGnTJv37ddddh4KCAgDAeeedh6eeeqr5MqcAK3ou8fHSvfhu0yH8+4cd+MvFw9wWh2EYhmEYJnvZ8QMw+XL1c/fDgV7HqH+9jwV6HQ30GMoKIJMUOTk5OPXUU/HGG2/ghRdecGwrhIj6nimum6zouQRnjGUYhmEYhmkB6suBz24Huh8BHHcFULpJ/dv8NSDCahvyAANPAq79GCjo6q68THLEsby1Fh6PBx9//DHOOeccPP7443jooYds265cuTIjFDszrOi5jGkCgGEYhmEYhkkUIYBpdwONFcB1nwD9RkTWhQJA+TagdCNwYDWw4AVg1fvAKXe6Jy+TVXTo0AHTp0/H2LFj0adPH9xyyy0xbT799FPMnDkTzzzzjAsSOsOKHsMwDMMwDJOdLP0PsPkr4PwnopU8APDlAn2GqX/DfwHsWQQsfR346R2Ah/MRMonRvXt3zJgxA+PGjdOzaj733HN49913UV9fj+HDh+O7777T1wHRMXo9e/bErFmzXJHddUWPiLwAlgHYJ4S42G150o0Am/QYhmEYhmGSpmQDMPPPwJHnqspbPE6+Hfj0FmD7d8DQc1tfPiarqaur0z8PGjQIO3fuBABceumlGD9+vO12c+bMaWXJEicTpjN+B2Cj20KkG+IgPYZhGIZhmNQINqpKW14n4PJXErPQHXsp0LE3sOS11pePYTIAVxU9IhoI4CIA/3FTDjfhGD2GYRiGYZgk+favwKENwOWvAoW9E9vGlwuMvhnYOhOo2Nmq4jFMJuC2Re95AH8EoLgsR9phex7DMAzDMEwKbJ6hWuXG3JW8C+aJv1IzcC57o3VkY5gMwjVFj4guBnBICLE8TrvbiWgZES0rLS1Nk3QMwzAMwzBMxlF7EJh6J9DneODcR5LfvnN/4NhLgBWTgUBDy8vHMBmEmxa90wBcSkS7AHwI4GwietfcSAjxmhDiRCHEicZsNtkOh+gxDMMwDMMkgaIAn/9aVdCuegPw5aXWz8m3A01VwLpPW1Y+hskwXFP0hBAPCiEGCiGKAFwD4DshxPVuyeMWgoP0GIZhGIZh4vPjS8COOWoB7V5Hp97P4FOB3sOAJf/mZAlMm8btGL12Cxv0GIZhGIZhEqS2BPj+MeCYi4FRNzWvLyLg5NuAg2uBvUtaRj6GyUAyQtETQsxpbzX0ZHkFnkdiGIZhGIaJw8KJQDgI/OzRlol/Of5qIK8Ll1pgbCEi3Hffffr3p59+Oqp+3vPPP4933nkHAFBRUYHzzjsPQ4cOxXnnnYfKysqY/srLy3HWWWehsLAQd999d9S6c88913Kb5pIRil57RD6j2GOAYRiGYRjGgbpSYNmbwIirge6Ht0yfeYXAyGuBDVNVayHDmMjLy8Nnn32GsrKymHWhUAhvvvkmrr32WgDAhAkTcM4552Dr1q0455xzMGHChJht8vPz8eijj+Lpp5+OWXfDDTfg5ZdfbvFj8LV4j0xSCLbpMQzDMAzD2PPjS0CoCRh7X/y2yXDSrcDiV4AVk4Az/tiyfTMtxpNLnsSmik0t2ucx3Y/Bn07+k2Mbn8+H22+/Hc899xwee+yxqHXfffcdRo0aBZ9PVaWmTp2KOXPmAABuuukmnHnmmXjyySejtunYsSNOP/10bNu2LWZfl156KcaOHYuHH364GUcVC1v0XIJj9BiGYRiGYeJQXw4seR047kqg59CW7bvnkcAR56jWwnCwZftm2gR33XUX3nvvPVRXV0ctX7BgAUaPHq1/LykpQb9+/QAAffv2RUlJclbibt26we/3o7y8vPlCG2CLnsuw6ybDMAzDMIwNi14Ggg3AuPtbp/+Tbwc++CWw6UvguCtaZx9Ms4hneWtNOnfujBtvvBETJ05EQUGBvvzAgQM49thjLbchIj0XRzL07t0b+/fvR48ePVKW1wxb9NyCC+kxDMMwDMPY01gJLP43MOwyoLf1oLrZDD0P6HoYsOQ/rdM/k/X8/ve/xxtvvIH6+np9WUFBAZqamvTvffr0wYEDBwCoSmDv3r2T3k9TU1OUMtkSsKLnMmzQYxiGYRiGsWDRq0CgFhj3f623D49XjdXbPR8oWd96+2Gylu7du+Pqq6/GG2+8oS879thjo2LtLr30UkyaNAkAMGnSJFx22WUAgCVLluDGG2+Muw8hBA4ePIiioqIWlZ0VPZdgex7DMAzDMIwNTdXAolfUunl9h7fuvn5yA+DLV2MBGcaC++67Lyr75oUXXoi5c+fq3x944AF8++23GDp0KGbNmoUHHngAALBnz54oK11RURH+8Ic/4O2338bAgQOxYcMGAMDy5csxZswYPblLS8Exei7DMXoMwzAMwzAmFr8G+KvTkw2zQ3dg2OXAus+Ai55RrXxMu6eurk7/3KdPHzQ0NOjfBw8ejB49emDr1q0YOnQoevTogdmzZ8f0sXjxYtx111369127dlnua/LkybjzzjtbTngNtui5BIfoMQzDMAzDWOCvBRb9CzjqQqDfCenZ5xFnq4plybr07I/JeiZMmKDH5dnx1FNPYcSIEXH7Gj58OM4555yWEk2HFT3XYZMewzAMwzCMztL/qIlYzmjF2DwzRaep/3cvTN8+mazm6KOPxrhx41qkr9tuu61F+jHDip5LkBalx66bDMMwDMMwGoF6YOGLwJHnAgNGx2/fUnQZqGbf3DU/fftkmFaGFT2GYRiGYRgmM1j2JtBQDpzhQu20waerFj2ehWfaCKzouYSM0eNnCcMwDMMwDIBAA7BgInD4mcCgk9O//6LTgMYKoHRT+vfNMK0AZ910CZmLRXCMHsMwDMMwDLBiElB/CDhjkjv7H3yq+n/3gtYr0M6kRNED01u0v10TLorbprCwMCrz5ttvv41ly5bhpZdewquvvooOHTrY1sibM2cOcnNzceqpp7aYzKnAip7LsEWPYRiGYZh2jxBqHbtBYyIKV7rpNgTo1B/YtUAtos4wNtxxxx2O6+fMmYPCwkLXFT123XQJ1u8YhmEYhmE09i4GKrYDo6wtJGmBSFUydy/gmXjGkfHjx+Ppp58GAEycOBHDhg3DiBEjcM0112DXrl149dVX8dxzz2HkyJGYN2+ea3KyRc8l5PODHyMMwzAMw7R7Vr4L5HQEhl3mrhxFpwHrpgAVO4AeR7grC+MqjY2NGDlypP69oqICl156aUy7CRMmYOfOncjLy0NVVRW6du2KO+64A4WFhbj//vvTKXIMbNFzCYVnihgm63lyxiac/fQct8VgGIbJbgL1wPovgOMuB/IK3ZVl8Onqfy6z0O4pKCjAqlWr9L+///3vlu1GjBiB6667Du+++y58vsyyobGi5xJSzWN9j2Gyl1fmbMeOsnq3xWAYhsluNv4XCNQCI69zWxKg51CgYy8unM4kzPTp03HXXXdhxYoVOOmkkxAKhdwWSYcVPZcQrOExDNOKLN1VgfcX73FbDIZhmPiseg/oVuReEhYjxjg9homDoijYu3cvzjrrLDz55JOorq5GXV0dOnXqhNraWrfF4xg9t4jE6LHCxzBMy/M/r/4IALj2p4e5LAnDMIwDlbuBnXOBsx6OFBl2m8GnARumAlV7gK78DM0EEimH4AbhcBjXX389qqurIYTAPffcg65du+KSSy7BVVddhalTp+LFF1/E2LFjXZGPFT2X4Bg9hmEYhmHaPas/BEDACde4LUmEwaep/3ctAEayotdeMdbQA4Cbb74ZN998MwA166Zk/vzYeM6jjjoKa9asaU3xEoJdN11C1/NY32MYhmEYpj2iKKrb5pBxmWU56z0MyO/K7ptM1sOKnkuwRY9hGIZhmHbNnoVA1e7MSMJixOPhOD2mTcCKnsuwuscwDMMwTLtk5XtAbifg2EvcliSWwaeptfRqDrgtSbuFExc2/xywoucSbNFjGIZhGKbd4q8FNnwBDL8CyO3gtjSxyAygbNVzhfz8fJSXl7drZU8IgfLycuTn56fcBydjcQk962Y7voEZhmHaI0R0AYAXAHgB/EcIMcGm3S8ATAFwkhBiWRpFZJjWZ8NUINgAjLzebUms6TtCtTbuXgAcf5Xb0rQ7Bg4ciOLiYpSWlrotiqvk5+dj4MCBKW/Pip5LKHp5BYZhGKa9QEReAP8CcB6AYgBLiWiaEGKDqV0nAL8DsDj9UjJMGlj5HtDjSGDQyW5LYo3XBxw2hgunu0ROTg6GDBnithhZD7tuuoSsn8cGPYZhmHbFyQC2CSF2CCECAD4EcJlFu0cBPAmgKZ3CMUxaKN+uJmIZeW3m1M6zYvCpQOkmoL7MbUkYJiVY0XMJVvAYhmHaJQMA7DV8L9aW6RDRKACDhBDT0ykYw6SN1R8A5AFGZFDtPCuKTlf/s1WPyVLYdTNN3P/Japw3rA/OP64vgEhsHut7DMMwjISIPACeBXBzAm1vB3A7ABx2WAbVIGMYJxQFWPUBcPhZQJcB8dtrhJUwPtz8IXZW77Rtk+PJwTXHXIPBnQe3hKRAv5GAr0CN0xt2acv0yTBphBW9NDFleTGmLC/GrgkXAYjE6DFMptIUDMPrIeR42fAfDyEEyOR+1BQMo7TWj0HdMzCbHOMm+wAMMnwfqC2TdAIwHMAc7Z7qC2AaEV1qTsgihHgNwGsAcOKJJ/JbhWlRih5oHYPyaZ61eC+3GHeXXYEvE9wHeeuQ3/9D+Aq3QQl1AGDt7kkePyav+wRN+69GqO44ANDHXSnhy1VjCDnzJpOlsKLnEpx1k8l0jvnLDAzr1xlf/W6s26JkPELEhpnc+d4KfLfpEHY+8fMYJZBp1ywFMJSIhkBV8K4BcK1cKYSoBtBTfieiOQDu56ybTFvhKu9cVIsO+FYZnVB7T/4eFAx8D+StR+P+qxCqPtG2LfmqUDDwXRQMmgx/2RkIlP6s+QIXnQ58/zjQWAkUdGt+fwyTRniq3iW4jh6TDWw4UOO2CFmB1e95zuZDAICmoJJucZgMRggRAnA3gG8AbATwsRBiPRH9nYjYN4xp03RCAy70LMG08KnwIzdOa4GcrovQoejfgPCgYfdvHJU8ABChrmjYfQcClT9FXs8fUHDYGyhvLG+e0INPBSCAPYua1w/DuAArei4hTP+Z1qUpGEbRA9PxybK98RszTJJY/Y7zfF4AQH0glF5hmIxHCPGVEOIoIcQRQojHtGV/FUJMs2h7JlvzmLbCBd4lyKcgpoTHOTekAPL7fYz8fl8gXH8k6nf+FkpTgvF8wgf/wSvQuP9/4C3Yg6u/vBqrDq1KXegBJwLeXHbfZLISVvRcQk/Gwpa9tFBa6wcAvDB7a9RyfyiMez5YieLKBjfEYrKMb9YfxFdrD8Qst7Lo5eeoj9cGf7jV5XLi9x+uxETTfc8wDOMGZ3lWYb/ojtXiCNs2lFOGDkUvw9dlFfyl56Jx702Aknysc6h6NBp23YlcTy5+9c2v8P7G91Mbc+Xkq8reLlb0mOyDFT2XkM8ahb26XGXeljJMW70f46etd1sUJgv49eTluPO9FRBCoKI+oC+3GjtkikXvi1X78ey3W1yVgWEYxoswTvOsw9zwCNglU/F23IKOQ16Cx1eDxr03I1B2LpozVFX8/fHhxR/itP6n4YklT+DB+Q8ipKTwTC46DTiwGvDXpiwLw7gBK3ppwGoGSVoAOFbPXbxe9WUT4jSoTBK8MX8nRj36rf7d0aIXcNeixzAMkwmMoB3oQg2Yp4ywXE++GhQM+ABKsCvqd92NcP3RLbLfLnldMPGkIa+wAAAgAElEQVTsibhz5J2YvmM63l7/dvKdDD4NEGFg7+IWkYlh0gUremnASocQDuuY9OHzaIpemC8EkzhzNpdGfbf6HUuLXkMKFr1pq/fj85XFKcnGMAyTiYzzrIEiCAuU4yzWCuT3/RygIBr3XQcR7N6i+/aQB3eMuAPnDT4PL696GdsqtyXXwaCTAY+P3TeZrIMVvTRgNduvcIxeRuAladFjH1omcbyeaLcjq994nmbRq08hRu+eD1bi3o9WpyYcwzBMBjLWuxZrxBBUoVPMOl/nlfB12gh/6fkQgV6tsn8iwsM/fRiFOYX484I/J+fCmdsR6HcCULy0VWRjmNaCFb00YOmeqS0Ks6LnKnLAznoekwxmRc86Rk+6bnLWTYZh2jedUY+RtA1zLdw2yVeD/L7TEGoYjGDFaa0qR4+CHnhozENYX74+eRfO/qOA/at4wMBkFazopQGrQWAkRi/NwjBR+LzqT4AtekwyxCp6VjF6MhkLx+gxDNO+OcWzAT5SMC9sVvQE8vt+BlAITQeuQjqGpRcUXaC7cG6tTCIj8YBRQKAWKOcsxkz2wIpeGrCy6MlF7LrpLnK8zslYmGTwxbhuxraRFr1GtugxDNPOGedZgzqRj5XiyKjlvi4r4Ou0qVVdNq2QLpx/WfCXxF04+49S/+9b3nqCMUwLw4peGnBKxjJvaxlmrIuty8W0Dma9Wl4bTsbCJENCMXqyvILLdfQYhmHcRWCcZw1+VI5DCD59KflqkN/nvwg1FLW6y6aZHgU98PCYh7G+fD3eWvdWYhv1HArkdgL2rWhd4RimBXFN0SOiQUT0PRFtIKL1RPQ7t2RpbZySsQDAw5+vS6c4KVHnD2FLSdurHyO4zAWTArEWPfvfeDbG6PlDYbwwayuagqykMgzTPAZTCQZ5SvFDVHyedNkMo2l/elw2zZxfdD7OLzofL69+GVsqE6g16vEC/UcC+1nRY7IHNy16IQD3CSGGARgD4C4iGuaiPK2GsAj/Mo4LPR7rwqGZxC1vL8XPnpvb5lxNdYseu24ySeD1RD86rX4W8p5qzEJl6a0Fu/DcrC14a8Eut0VhGCbLGetZCwCYpxyvL/N1Wa66bB46HyLY0y3R8NBPH0Ln3M748/w/I6gE42/Q/yfAwbVAKND6wjFMC+CaoieEOCCEWKF9rgWwEcAAt+RpTaxj9CLLZIr/TGbxzgoAyStEmw7W4Kyn56Cqwd2Hot0pltcmFOZkLIwzxt+s2aJnmXBJEbbrMp0Gv2qF9IeyT0llGCazOMOzBnuUXtgt+gAAyFeN/D5fqi6blae6Klv3/O7485g/Y2PFRry59s34GwwYBYQDQEnme2IxDJAhMXpEVATgJwAWW6y7nYiWEdGy0tJS8+qswFLRM3w2x/tkMsnGsr343TbsLKvHvK1lrSRR85CD8fZu0ctG98J0EzTc+15vfNdNeU9l460lZfZkwSQUwzCZiw8hnOJZr5VVIAAC+f3cddk0c97g83BB0QV4dc2r2Fyx2bnxgNHqf3bfZLIE139hRFQI4FMAvxdC1JjXCyFeE0KcKIQ4sVev9GVkakmsBnrGgaHH9auQOMFkyxBk+CBXXhslG0fjLcTWkloM++s3+GxFsduiZDQBg9U3mRi9bHR3lrJn0RwUwzAZyE9oGwqpCfO0+Dxf59XwFW6G/9AFrrpsmpEunOMXjnd+ZncZBHToCexbmT7hGKYZuKpiEFEOVCXvPSHEZ27K0ppYPTSMi7LBdVMvQ5BidspMPUTddbMdK3obD6pJdr7bdMhlSTKbYCii6CVSMF3+VrIx0Y/8OVCm/nAZhskKxnrXICQ8+FEZBkBBbs/vEG7qi2DlKW6LFkW3/G74/ajfY135OszfN9++IZHqvsklFpgswc2smwTgDQAbhRDPuiVHOrC26EU+Z0MyFjngy9ZYNruxdpgVvay0OLlBMEmLXlhkr+um0C16mf9sYhgmcxnnWYNV4kjUoCN8nTbAm3cIgfKzkAEOZTFcfPjF6NuxL/6z9j/ODfuPAso2A/669AjGMM3AzV/aaQBuAHA2Ea3S/n6ebiFqm4KYtHBXqw52rWf0sysZixzXBpMctYoM990UnIxFh603zvijLHrRj06rn0VYyV6LnpQ4C+agGIbJULqiFiNoJ+aGRwAQyO0xB0qgB0I1w90WzZIcbw5uPu5mrDi0AitKHGLwBoxW06kfWJ0+4RgmRdzMujlfCEFCiBFCiJHa31fpluORaevxyLT1WLi9vNX2YRm/Y9ArsiEZC6F5Fj25vVvYjbXldchGq0tLIc9N5t+F7mK06HlNT06riaJwFmfdlDGrbNFjGCZVTvOsh4cE5inHw9thG7wFxQiUnwHA67Zotlw59Ep0y+vmbNUbMEr9zwlZmCwg82znaaaqQa2b0pqFga0GekZLV7KDqUM1Tfh0eXFaXe6kiMEUY/TctuzZ7V8q4cF2bNGT54bH9M4EwqlZ9DLFNTYZOaTbKd8TDMOkyljPGlSLDlgjDkduz++hBDsjWD3KbbEcKfAV4IZhN2DevnnYVLHJulHHnkCXwzhOj8kK2r2iJ8cxrTkWs87IF/mcbNbNid9txX2frMb3m9OXPEMO+ELJZt3MEGwtetqKcDs26WWIHpLxBENOdfTsLXqzNx7CtxtKWlc4A4oiUFrrj12exHXWrbys6TEMkxIC47xrsEAZDpG/D76OOxCoGAsIn9uCxeWXx/wSHXM6xrHq/QTYxxY9JvNhRU8bx7RmHI3VAKs5WTd7dMwDAExdtb85YiWFR0/GkmLWzTQ6Bq4trsalL81HYyBipbWTWl6bcDvWduShZ4ub3uQfd2FfVWPa9xtt0TMnY4ltLxW9Wn8It72zrFVlM/Lq3O046bFZMcuTsVpLxdUb55ao94dwzwcrUVYXq1gyDNN+OYL2oz9VYK4yArk9v4cIdUCw8mS3xUqIzrmdcc3R12DmrpnYVb3LutGA0UDVbqC+9cJ+GKYlaDOKXlgRUQN7K2ZvLMH7i/eYlrb+4NayYHozFIscbfSVTndDeZaywcXx0S83YE1xNdYUV+nL5Pk2n/dIrbP0yZepZIOaV1kfwF+mrscNbyxO+74DDuUVnLJuppsfNpdaLk8ms2yiTacsL8a01fvxwqytCffNMEzbZ5xnDQDgB18/5HTaiEDlqYDIc1mqxLl+2PXI9ebirfVvWTfoL+P0uJ4ek9m0GUXvwc/W4Ni/znBUoG6ZtAwPfb7Wcl1rDsks6+gZPhuz+SWC1LXSOY7ULXrZ4OKoW2kji+JZ9NozmXQKVu2tQtED03GwuslyvZS1sj6QPqE0jJMc5t+edcIld86sz8YMl0wiJd2lOc4hSOU319dmXiUMw7QAYz1rsV3ph8oeqyGUXAQqTnVbpKToWdATVxx5BaZtn4aD9QdjG/QfCYA4To/JeNrM2/njZcUAgIY4Vj0z0lutdWP0rJZFFiZrJQu7YYXSk7EkJ6uUMZ3JWCJxl5F92mfdzCQ1xx3089SCJr3apiDW769OuL0/FMYlL87Hbz9QYx4Wbi+zbOexUOLThdGiZ76fLQumu3RvmRPFSJJJpKS7NMeJyZXurDnmNKQJMHnRbuytaEh6O4ZhMptcBDHGsxH/9RwNX+fVCFb+FFA6uC1W0vxq+K8AAUxaPyl2ZV4noOdRnHmTyXjajKLXvWMuAKC8LrmZ/sjYNr119IyLAkkqT1I5SadrWHNj9NKZ7ETKahqKW7ZtrdjM5bsrUfTAdFcGsjVNQewpT3y/8gy0ZBzlrZOW4aKJ8xO+7jvL6rF2XzX2Vqixd/d9strxGNyoTedk0bMSx60EP+ZEMZLk5NHqS8bZRp6TZC16NU1B/OWLdbjxzSVJbccwTOYz2rMFHciPad0IEB41CUsW0r+wP35++M/x6dZPUdlUGdtgwGg1IQvHfjAZTJtR9Lp2yAEAlNcnlxQgLRY9Cz1ODlQHdC2IyuZnx6GaJvhDqrVSKnjptEY1N+tmqgrif+btwLp9iVuGgEgWU+M1tc+6mZJYcflwiRoLameZak0u/9cCjHvq+8Q30DMsGhYl+YOo84eiMj0u3VUBIHGFzKxkCgE8+PmamHbyernxXg0abha7WE8jbil6dkl1krHGS9nDcX63uutmvKwtJmQ5mzp/KKntGIbJfMZ51mCfJweHOu9CsHo0RKiz2yKlzC3Db0FTqAnvbXwvduWAUUD9IaC6OP2CMUyCtBlFr1sH1aJXkWTsTjqyQRoHgQ9+thZFD0yHAHBEr44465hecS16Qgic/Phs/OGj1Wp/SmKz7S2JHDwmW0dPHnqqg95/TN+Ii1+cn9Q28poaz7t9jF7rnEN5bVJxaWsuO0rrk2qv19EzLkvytDz9zeYo64xMy5/odbfSTawUFnm93LDohZKM0UvV4t7cunt2t1wyzwt5qPGOIZii66Y/KBXENvMKYhhGY6xnLZ7rfBhAilYgPXs5vOvhOPuws/H+pvdRHzS9W/tz4XQm82kzb1mp6EnXzQXbynDiP75FfYIzxq2bjCXy+QPN0iOEABEh1+uNiv2xQiZrkbW45OC5tQe7obCCci1tOunLUnTdTEHWZJJHGLEqmZHuGD2pvPuSHMgKIfD63B36eU8HkZppkWXJ3luVDQGU1sYmUElY0UtwP0qa7n0rjIqSee9TV+3H/K3R1ttUJzfCDpbDRPDZxOgl83uSsXnxjkFO/CTruim9E/I4iQvDtCl6oBqDfHswu0sYoZoREMEebovUbG49/lbUBmrx8eaPo1f0HQ54crieHpPRtJm3bDfddVNV9P45YxPK6gLYUlLruJ2Vm19LYxej5yF1gGRn0QsrAuv3V+tlI+SgKJIooXWEfm3udkxdtQ+PTFuP0f+YhYZASLfQpOy6mYKsyWYjlUhZoy16djF6Ke0iLsEUXdpW7KnEY19txIOfWWeHbU2oGRGrduVNElXwrSx6VsW65fVywytSTnIQxT4v3l64C9ebSj6krOgZOk+lC3PpB0ky1vhggl4D8jearEWvKcjZOhmmLXKSZzPe71yIkEdBoPxMt8VpEYb3HI4x/cbgnQ3vwB82TML68lRljy16TAbTZt6y+TleAECFFqMnBzvxZv7l4LY1s0Japl4XAgRCrpcQCCmWM/fPfrsZF02cj1VaPbi8HI++LdB6it7jX23C7z5cha/XqSmFGwNhfSBuNVisqA/g42V7HfsMp2CdS1XR81jIah+jZxhUN+N87iitw+NfbdSvY6qum7VNqgW6MZhc9tjmYHXUyU58KEKgyeI+TvycxionVuqKfr1cUPSklctLlNDzItXfp3EuJRXLZUskY5ETFfEteqkpbPL+ztOe2wzDtA1GeDfhvc6dEa49Goq/n9vitBi3HX8byhrLMHXb1OgV/UcB+1dZJ2NgmAygzSh6ckAkyytIRS9R/SLd5RWEUC0DcoBkpUAt26VmedpfpWYizPOpgyI9UUIrmzXkoP2Jrzfpbl9W7l+//WAF/jhlDXaWxcaGyQFxKh6fMmGD3cDVDqvi7nbX16iYNCfm8bZ3luG1uTuwW8sUGXRw3Vy+uxIb9tdY9qO7wrVQ7NK2Q3Vx3f/kaqPHX7IKRigsEFZEzH3cnBg9q2VuxujJY/N4KCFLW0tY9FLpw9ail8RARN6/W0tqHV3LZTu7BDB26F4KXg/2VjTY/h4Yhsku/J03o9rrgb+NWPMkJ/U9CcN7DMcHmz6IfqcOGAX4a4Dybe4JxzAOtCFFT/0vrUAJJ4OQWTdbSzBYx9koQpVRWnysMuLJZfIYpOumHAimq7zClOXFqGwIqjJZnM+SGtWK6pTVL149LivktbQrAG2kuiGIZ2duRlgRhsQx9nXPInI1b1AtibgUqh/k4Njq2v/ilYX4+cR5lv3I7WZvOoRX5mxPSoaF28pQ9MB0/fu8raU499kf8OmKfXFklzKmnphI9mG2RDbnHrWSRpjOczqRbss+j4XvpgUtE6OX/PZ2il68+Nr5W8uwfLeaLVUqtd9vLsUj09bZbiPv12SvR8Si58HYf35v+3tgGCZ7yEMASzs3ops/D+HGIrfFaVGICFcddRW2VW3DmjJDRmhOyMJkOG1G0ZMDamkF8lrEaVlhVVy7pbEe7wl4KDITbiWntDCFTAkP9IQULgQqWVn05LlzUhNSi9FTr2WOTXIJI3//cgMmfrcN324oibiZhuIPmI1ipRp/CBgTwMi+UrM8yWMGgCdnbEpq2zfm74z6vvmgGp8az1pide8bFz31TXw5pHLSZFb0mnGPWsfoyfOacrcpI6+p6roZn1SVXOPvOpU+7BU95/v7+jcW4xev/AggepJk8c4K221ku2Sfn/I+4WQsDNN2GJq3Amvzc9G5+mg0Z+IwU7lgyAUo8BXg862fRxb2OhrI6cgJWZiMxee2AC2Foit6WhyNJzGLntVgsqWxjtFTlQNysCgGTHEyMp5Ffk9neQWJlVVAtwcRsK+qEeV1fowY2FVdJy0wqSh6wcQtenLgGAwr+jW1SnJjlkJpppucxDxhELHGJtdPvAysjjKYTlOiFlF53HZZN2esO4j/O/8Y5z605vI6yK4SPadWioJ1jF5C3bUK8t73eklPpvTh7afg6n//GNNWCJFxrpvJdGVU9JwmWgLh1BRv6brJyVgYpu2Q320JfEJgf9VZbosShdHTpbnk9TsOUwL/xdtfjgCUPADAR7mDkLtoNq6Y23L7sWLXhItatX+mbdJm3rIR1011AOGRil7CFr3Wkswu66bqYiiVEqv9y8FWo2n2W3fddGHU6xTnEwgJnDbhO1z60gL9OkgJU1FKdetsAhY9owtupBREIjF6kc/J1gg0ErHMqt/NSnqimBPQBEIKapqCCW4dPcCX94/dwF8ir01UHb2ofuIfg1Tkza6biRpJrU6TU4yeG4QM8WgCankUu1DK5vw0jZMiqXga2Ct6ifdlvOZOEwUB7XeequumMRa1Nb0qGIZpZSiEvZ0P4OR6BRXhtpOExUyw6iSQJ4CcThH3zdXKERhGu5GDxMp5MUw6aUOKntl1U1se16LXqmIBsFYyFKEOrJ1cR+Vgqz6gPjykopdqnJIQAtsO1SXcXsblGbGM89EWNQQiDzkpu2iGUupPokSBsYVVcXfbGL041pPZG0tw5csL4t5HZhfcQIoubeY4x5veXIIR42cmtK35XpbKpjfOTS5lNjYzyu0UeymR585cYiFR10PrZrFyu6kMSIVYCKFb9Ozck5ozCdNci55d8qLkFL3INXeqBRlM1aKnT15Fsm5KbwxATQLDMEz24CtcjwavwOE1bVfJAwCl8TCE/b2R03WpvmyNcjjyKIijyDn7OMO4QZtR9OQYJmnXTbl9mssrCKhuo7rrpsXupQVBFn3Pz2le1s0vVu3Duc/+gDmbD9m2iTeQdorzMVqjzO6lTrKu318dE9tl7C+RouMRy6jQz2kgAYue8dpYxejd/f5KrNhTFbfcgcd0v0mFONkYK7NF78cd5Qlvax7eJ2rRk6fJWEfPeLmSUvRSjNGzjlGN3a+rrptS0YOcqCHYndpmKXqGbVPpxtPCip7TREvqMXqxbsX1homi856bm1R/DMO4S6euC9E3FEJV3Qi3RWllCMGqk+DtsAee3BIAwCpxBABgpCe5BGoMkw6yXtGbtaEEW0pq9UGM7rppsrB8uWa/5fZ6ce1WLIFiXV5BVUgiimYsMsNlg189pm83lODWSUuTct0sq/OjulG1zK0tVpNyOFn14rlYWmXdlEuMyprZkmcna1VDABdNnI/7P1kdsy6Z8gpGF9xIzT9j1k1rjONTKxkTTecvRZTnz5wxNVFSrR0IOFj0PITd5fUY/ei32FvRELOdlUXPeMIScd2U96RZYU9UubBq5w9aKXqpK1DBsIIPluxJOYmRvKaKItSJIbIvK9CcOoitVUcvmWec8RT5LFynqxuC2F5aF8m6mcQ5nbHuIGZvVAdIxq3khBbDMNkF5VQg1HE3rqitxwrlKLfFaXVC1T+BEF7dqlcseqFCFGIE7XBZMoaJJesVvVvfWYafPTdXH5iYLXpy4H33+ystt3dStFoKu4LpHiJ99t3adVOz6BlmumdtPKQPqhKxFp34j1k45YnZUXI41byKZ71xsugZB7dmBc9OgZR1D5fvroxZl0x5hUhSG6EPaKPr6Nm4bhrkspLRqTj9hv01uhIt9y/Pj64UpJiNMBXIZNMLGCx6U5YXo7w+gM8sSi1I62O0npea62aTSTlLPBlL7DJjBlJJcyZkXpmzHQ9+thZTVzuXm7AjbLDoaXqe5W/p+82HMOrRb1OWs/nJWKwf68nci8b9Wv3+Xv5hG37570X6PZaMmHe8uxzrtUywRgWx3p/6vc8wjHvkdFkOADi3NoStYqDL0rQ+IlyIUO0w+LqsACgEgLBGOQIj2KLHZCBZregZk1SYY/SkAhUMKwm5FaVqKSiv82PSwl2O+7BaF1ZEVIyeeaC0Yk8lqrQYuQZz3JPWeG9FI26dtBTxkNsLK8uNiXjWG6v1sl9jfJYcrCbiummHHOhbWRTMyENasbsKM9YfVPcdFaNnTZTrpsWxOSmqP584D9e+vghAZMAfsejJ444rehTmGLdkiLXoqTJ4PaTLZzU5IJcZ1yTruqmY7gEpS3NcN62ypjbHordHs2Yay25IdpTWWVo7jej3vpCu19a/pR82l1puP/rRb/H63PgzvtGumykcr802yfRlbJtj4TpdWuNHWZ0ftU0hvb0/FE7oXrHbj3FCi2GYbEFBTtdlOKEROBg4HEp2DysTJlh1Ejy+BvgKNwAAVovDcRQVowBNLkvGMNFk9S/yya/V+l75OR7bOnrBsEBNU/QAIkrxSjBpCwAUVzZgd3l91LL7P1mNR6atx6aD9skDrKwQiqIpBzK2zKSKXPnyQv2z2aXJODiatdE+3s6M3Ko5Fj2r9brrZshoQVP/x3PddDrr0nUvJyGLntpm8qLd+rLEYvQin51iwsxKoLyHpGVC7l+2M7r5JYOdy5/dRMIPW0pR9MB0bCmpjVE6pAw+D+kWbit55DLjLqKTsYi4kyXNj9GLXWblutmcXCx12nOgMD+2qszZz/yAsf/8Hs/M3Ix1+6ottw9r94eA5noNcvwtmSmvD+CxrzbGbWc816lYMO1OeTK3ovE+sXIFrdWeSaW1fgDqdRkxfiYunjhfb7O/qjHmeekkE7tuMkz24e24FZ6calxfW4bl7cBtUxKuPxJKsKvuvrlaOQJeEhhOu9wVjGFMZK2iV1kfwHuL9wAA+nUpiCRjCUW7bgbDCsrr/FHbGgef0t0tkUHQ6U9+jzOemhMth43VzYjVTHpYqL5f+hDKYf92Fr1kibhu2reJp+hJV0Ur/BaumyEHixjgrAg1SYteIslYLJZFH4uNopmgm5xZCTQ3lbqoLD+hx+glm3be5j4yynaopgm/eGUhSmv9mLFOtV4u2VkR47opZfBQRNGzkke3uhoVDFOzeJZe24LpzYnRs4hXbI5Fr05TJCb/uNu2rtKL323Dla8stFwXMmSSlVk3WyNrrzERSSrHa7dNUq6bRoueRa27WlPJD9Wip2CzIVvmqRO+i3lexsikCOTnqP2bPQ8Yhsl8crouhTeUh7MbGrBctB9FD/AgWDUa3o7bQDkVWKOoCVnYfZPJNLJW0TMORPp1yY+ktQ8peo06QB2cTVsdnYjFOIjVXcxSHFxIa5NToWsr/SGsCHgMyRzMTTrlRawOZmthqgn99O2cLHoWbm1GqixKLshTF+W6qVvynC1bToqltOjEKw8AwFLTMx6L3eWNF6Nn1c7quyfGopeay6qdIm2U7d1Fu7F8dyXeX7xHr+MmtIkDI3qiDMPvwdKiJyIKjMRsYbaydlr1EZOMRRH4ftOhuLGHVoN7q99USyh6MpOp3T1pF4cqExEJaFk3KTmLXiKYZUr2ufT2gp14eY71QCMpi56h7fQ1B/CEyRJZF+NlEPl8oLrRtl/zuVWE0LMJyz7dzKzKMEzikLcOvk4bcExNV/gEYZWm7LQXglUnAlBjFMvQBftED5zg4YQsTGaRtYqeccDXvWNu1ODAH1L0AXBtUxDPz9pqu61dHbvSWj8+X1kcVw4ZP1bnD+GHLaWoagjEtLGP0YuUVzAPYDtZuJdZyZ8MUg4ni55VXJSRqsbY45M0RmXdVP9LxcdOiXJShPwGRSUeZmsWEF3c3a4H4+6dZDFbtMxtIzFp0TGhyVyrhkAIS3dVWK6LskIbMsoaC7Wbz0DAkBDGZ0pOZNV31DGZLXpxJgBkv2bXzTXF1fjV20vx9y83OG5vPE03njIYN4wZbJ2MpTmumyblxE6JIhvlTZ+0EGrWTTUZS+ryWFFjtpQlecDj/2t/npPpy9z236bYwrome3fynaX27pr1Jou1IoACTdFr0K5Pc0pTMAyTPnxdVoBIwRW19dgsBqEOHdwWKa2IUDeE64cip+syAArWKIdz5k0m48haRc84ECWiqMF1UzCsu6pZWaCiFD2bpBG/nrwM9360GgernQNrZUa6fZUNuOnNJfj15OUW+4vdLqwIeDzRZQGMdMrPsd1n4pkMo9tJvcdshfCHwli4vQxAfNfNynoLix5iB/nmEhBhG4uQk0ugtALFK/kAWBspowqmi8iyqav26ecm0cLg5nNutnCRISbUqFAkM2hdvrvStryC8RzoVmCDohdWRIyCIvvaWlKHZ7/dYiuPXGZcZW4WbwJAKgaNgeh2VZqFcrtDSQ/z/rweQp7PA39IQSCk4G//XY+KenWCwXxPbympTSjBCRCrnNhdGzvdLai7bmr3E8UqhWuLq5NOqDNz/UEs3F6Gg9VNKK+PnkhpSZ0nVddNK2od4p6d7pWGQKyyLS16UglsjtWWYZh0IZDTdSnCDYfhwvBOLFeGui2QKwSrToInpxrejluxWjkCRZ4SdIV9zgaGSTdZq+hFD0pF1PemoKIPwMwz5IHkG60AACAASURBVOZtpSXob//dgEWG4tT7q1QFb8wTs7FiT2zqf4m0lJRqcYDbS2MHtJYxeiaLXozrpoNFL1HlwTzgsovRe+KrTbj29cVYt686rqJnZbGUNDmUV7DT55yORQ4KE7PoxRKMSg6j9lFW58fvPlylu/Ma+3a26JnOpek0ReroKVGTC9+sP6jXCAyGlRirkhGnOM86fwj3f7IaFfUB3VqtiOh6kXYF0z9ZXqwr4Xb3onmd2XUz3n0hFYOYZDJCGP/ZYs7ymJfjQSCkYPbGEry1YBce1SyC5kv0i1cW4rGvNiaU7dF87hOZQIhqL4uDa9+tLHqXvDQfHy3ba9uHObFJWZ0ft09ejmtfX4wxT8zWFVpJcWVDszKxGkn0cIUQca1/tQ6um06TN+YSCkIIfVKu3p/4751hGHfxFuyGN68UPaqPQGdqbFeJWIyEao+FEuqInK5LsVrIOD226jGZQxYrekZrjYj63hQM64PemsbYgXW0G1xk+buGjI1GFu+wdqcDIjWrymrVAZo/qETFFgXDim0yFjJYBMyWipZw3TTHOOkDVJMVQhZQv/jF+dhaYm958XoI9YFwbL9ax8YaavJ4QnEsek6xX3KGPxHF1sqiF5V107SuvC6g9W2URaC6IWiZZj+eRc9jsOgZFb1FOyowZXkxhBD4f28vxfBHvrE9BqfjfG/RbkxZXoyJs7fq1y8shK5oCGFfMD3ePqSSZhzcm28xq9ITRuTpkMq+nECxSvRiub1hvddDyPV6ETJYKWs0y6D53pf7S6RAeYzrps0x2YXd6Yqh0LJuJhCjZ84Y6zEpeuZrJO9LyS2TluGWBEqoJEKirpvBsHBUCv2h2GeA8b5yUrpjMggrEbkakvi9MwzjLjldl0KEc3GKNmRoX4lYjPgQqv4JfJ02YB31BgB232QyiqxV9IxDAUWJnlEOGJQradErNCQ3scvqJgeVE77ehIM1EZfN95fsxpEPfWW5vVQmpEWv1h/C41righnrDmDow19js0XpBelqJ4d9X689iEkLd+nr83xeSxnltolgdgO0K5hu/Pr0zM22/fXomAsg1qoXUfTsXTetFIU95Q044OAaW59EzI5VjJ5xUGm+5PJcRFn0wgI/nzgPY//5fUxfZuuPUabPVxZj4XbVGhwKC8s4Rn9IwbytZY7H4DRAlgNrj0G5UITQFYewhUXPKgGK1blUdIueYZnphFm54x2qbdJLEdhl3Ux08G7cnc9DyNMyMUprT4ONW5+MkU3F6mU3yWB1LwGGrJuavTORrJvmGpBWpQqMmC16APR7q7koQiAUVvDlmv0or/NjyU7rCaxgWHG8XmYXWCBxF2hzrbywEPqzQk/GkkJJCYZh0oinCb7OaxCsOQE/pR0oFZ2xR/R2WyrXCFadBCIFTV02YrvSDydw5k0mg7A3G2U4xtlpRUTX+QqEFH2wYFT0rLK6GQdqcgz26g/RP9K9FdFZ5IJhAQ8JPD1zCw5UqesO1UYUFqnYzVxfAkBNSGFGZt2UyqWsr3XTqUXqegcLSBzjio6d5c081DQqfk6Wke4dc3Go1o+qxiB6d86PWd9oWV5BifpuZNxTsQqVpCkY1ge9qc7wVxoU0tgskrExeiFFYF+VdcbAGesOYNRhXaOsaZJ7P1qtfw6bXDcliSgiTscpFS2vJ3KfKkpE0VNEbIxek0UdOqcYPeMxmW8/q8H7ec/ORXVjELsmXGTruindb+OlzDdb9PK0lP7SXVL2a+4mx0toDKam6NmebxtdLKzfM5HznYhFz5hI1RtX0fM7rm8OigCe+XYLXjFk5dw14aKYdqGwcHz+WLkfR020OWQgtnLdlOd1yvJinDykO849to/t9gzDuE9O59UgTxDBqpMwip7DCuUo2Ec3t32UQB+EGw5DTtelWFl7OMZ51kM1R7Tfc8JkDtlr0YuyPkQPFP0hRR+oSNdNoytk9ACPLD450xQKY+aGErz6w3Zs1dweD9VEBmglmtLnqKwpqgXGbp+hsKLXlzKTSJ2pYDhW4dCzbpq6NY49nQbMHTWrqN1AzritFNFKiUiEMU/M1hVkqwH5kp0V+M27y3WF32q8XWFMHGPqwioBiXE/5vPw+ryd+Gb9Qf27nSuj6roZa5VxUqCfnLEJN7+1xDFmTLfoGYufi0jpCYvqCpb7tLoOYZPSe7C6CRsO1ES1sVL0jKUgIslYrC168XR14/ocrwe5vkg2W0BV/JuCYTzxdXSa/xxvdA22MY/PjpmoscPpfM/aUBLj6hg0Zt3Uznc8i16OqQak10NYt686kgzI1N6clbIlCQuB+SarspU7ZyCsOD5jZCIW4zPV+Px1itEzJ2NRRPTv7o9T1rDrJsNkODldlyLc1AddmzpjiKek3SZiMRKoOgnevFLMye2J3lSFvrAP+WGYdJJVip4/pBgGSEbrg4hy91EtetGum8ZBSU1TEM99uwWhsBI1UPti1X68MX9nfDmCsa5NVYZBb4nmjug0XpGp8Z3igXJtCoUnMhC66c0l+PnEeaZ9qv/NrmlGq4Rd1kcAuuJpl1VvscEVTMqYaj05o5JqpZz8evIyfL3uIKau3qfFS8X2UdkQsB1QKxYKqNGVz8r9stQQP2V3PP6Qgg+X7o25dk6K3itztmPO5lJni14oUlPQqrzC1FX7YpK5WLluWg3szW62Y56YHZM9Nl6yE6k02bluxosrjbXoeaO2bwyG8d7iPVi3L1oBlYqUPL8Ha5ow4etNap+KQFmdvYXM7nwHQgpufWcZJhtidsvq/Fi5pwqAei8JqEpePIuezxSjV9UQxMUvzsdbC3apMpiUIrsafi2BEAJbD0W7kQct/CSl6+atpw9Br055MeulolfUo6O+LByl6Jk9CSLrzNk6w4qIuQ6cjIVhMhdP7iF4C4oRrD4Roz3bAKDdJmIxEqoZAaHkYGUn1SuI6+kxmUJWKXpbSmrxyXK1tl1s1s1IrTBjAhRpdTCWK3hqxma8MHsr/rsmupA6AD27nxNNwXDMAM9o5aoPhFHnDzkOWNREE/YDxbAikGsTp5eI0mQV1yPlCYYV1BqykdrVDTMj612ZrVmWdQK1ZbIWWrxkHk5YHa9MyX7vR6vxzfoSy2MIKwI1TdJ10Fq+KNdNg4xWZSSMe7CzBq3YU4n1+2vwxwuOjlpuZSk1K11OFia/Nnh+ec52vPTdVk32iDV2S0kdZhgsjoC1culUR8/ptnKy0gAGi55pnwnHWRpW+wyum9Ki1xAIWyquOT71BDQGwjHn84Ole3DiP2bh+82H9FhCI/Jc2CUp2V0eScpzy6RlEVGFvJ/iu26aY/Qkq4tVpdE8iRHvPDcHRREx7rxW+1Ofn0CHPJ9lUih5TYp6GhQ9Qz9mRc+4D6MVGIg8u42wRY9hMhdf59UQghCqOQGjPVsREF6sE0PcFst9RB5CtcNQ3Wk3GoSX4/SYjCGrFD0AWLitDIt3lJtSwasDLzn4D4QUPY5NNjMOWOSAOBgScdO+W+EPheMWSi6padKVCOsab2oJCPM6Y7bKXK/1TlIvmK7+f/CztTh+/EzcOmkZ6v2hhIs+52nnN2bG3nJfatykVICbM3iz2lYqnQBwsLrR1gW20iK5hbHPKEuw4bhmbjhomUQHUK/tuc/+YLnuhy2lAIArfjIgarlV8W+z0uV3sPoZJxIqNWtn2BCjZ4XVbeJUXuHbDSVYvtu6lEii5RXMioS0yCVTXsHnId11UyqKTYGwpQw5Hum6GYqxNO/Rsqf+6q2luPmtJbEyaxffyqplXA8A+2NiN+VEjdNR2cfkRX4X0ft2ykLbXKx+gkELC77Remz8cT8zczMOVjfpk0TjhvbEkb0L1X4M5958HYzrahrN7uTRv+8uBTntQtEjoguIaDMRbSOiByzW30FEa4loFRHNJ6JhbsjJMNEI5HRejXDDEIhQZ4zybME6MQR+5LotWEYQrDkB5GvElIKBGEGs6DGZQdYpel+s2o9fvrYoagAvY/SMroXmWXrLAuSUWLybGWOdPjtKqpscs8dVNQQxxDAjLpGDpFBY0Qe7ZuINhKzcTw/VNmH62gNq39r2szaWYMa6g3HjjCT5moXRPJCzOoVhRd2PFLWk1rnwvBOWil5uRNFzOhsyIYtdMhbFUMfLWFri+Vlbcf7zc6O2kedp1d4q2/0FQgqO6lOIHoXRLm/mQuJA7HGZrR1GrFxq1dp5yQV7OyVjAdS6dFY4KXrGhBq6RU8Tqz7BWohGsbxej27Rk4piQzBsKbt0jWwMxiqCXQoiv/myuliF/5Fp6+EPhe3jLQ37621yYVQURCVTsiOeomdW9ptj+Y6H1TWwuq5+PcNr9DYvfrcN93y4UrfonXVMb8z6wxkozPOhzPA8DobMVsrIPsz3+PxtZfrEBaAmfGrrrptE5AXwLwAXAhgG4H8tFLn3hRDHCyFGAvgngGfTLCbDxODJ3wdPXhlCNSORgxBOoB3stmkgXHcURLgA0zsWYoRnJwicQphxn6xT9CTGQZ+soyfjegKh2Np1nS1ckAjxLQ1WPDljU3yLXm1T3AHL6MHdbDMlqq6b1pfHSc8TQli6n97y9jKL1sDLc7ZZ1hq0ooOmXFlZAcyEFaEPGDvkerG3osHS9c7M5oO1MYqqk+smYF1DTqIreqYujDF6stbZmwtiFWQjUqmK5653TN/OMcus3ShNNdRsrI8AELCwCM7eeAjPzdriKIsZK30tkYF1IGTfJqxE3O+azMlYtCyL8SyCZoue/C3XGVw/LRU9Q3kFc5Igp+yPALBgWzmmrtxvq1wZ4+XMsWphTcmON0li95yQkyXmY7JypUzU4h4PRcTG/Uo5jBNe8ndqZS0uq/PrcXayZE1IUTB9zQG9TTKum2a6dWgXFr2TAWwTQuwQQgQAfAjgMmMDIYQxGLUjnOezGCYt5HReBSG8CNYMx3G0C3kUZEUvCh+CNcdjc2EDcjyNKKIStwVimCxW9AwDEzXOA3rtrYCpDlSuz4PfnHlEbB9KbHxIIsSrhwYAJTX+uJkGhw/oEjOIk4OskEnRM1oGdpbV2/ZpVcoBANZaxCgBwPbSevy4I7E6XYX5cmBnitGzGIMoQuiuiMf17wxFALvK7eWWR3fJS/NjFFWrZCxG182mUNjWsiUVZ3MPxlT5dklvYmTUdhGv+VF9CmOWWSl6Zovv+4v32PZppQAYaz0myqyNJZi6al/UskQG1k4uhSEl1qInr4bMsmhOFCP5z7wdKHpgelQMo5XrJhCJa5MIIZDjiyRjMVuanRILScrq/baumyEni55wjrGV2Fn0gmEFNU3BmJp0IUVBr055Ua6/icTQhhWBFXus3W4jMiNm8ki6JxtvAXnevB6K+d00+MOobQoh1+vRJ1tilVV71834il7bt+gBGABgr+F7sbYsCiK6i4i2Q7Xo3WPVERHdTkTLiGhZaWlpqwjLMCoKfJ3XIFR3FKB0wGiPOsm4gjNuRhGqOQFhTxg/dCjACey+yWQAWavoGWfhFUUd9OXbWPQ65HrRtUMuXrhmZFQfDYFwXGXMDqcsioCaot7sFnpY9w5R3/N8nhgFpaYxiC9W7kMoHD377lR/y7if/319UVzZU0XO4MezzgCaoqcNGI/tp1q4dpRGFD07l1krK4w5MyGAqNITVhbJc45Ri7fK2Djz/qTyqAj7pDd2xBt4D+3TKWaZ2dIFJBePFc86lQzPzIy2AjolgZE4XXPpouv1EEKKiGorFbw6f8jS5fXVH9TMZMaSFD5vbDIWQLXAGVEE9DjWhhQseoAaw2l3bMbtu3WMjkGR5RXiWdvsFMFASMGI8TPxy9eif6+hsECnfB9u1uppAqoidfYzc7C9tA52/Hvudlz5srXbrbEfM7dMWoZ1+6qj1snfjIdiLeH1gRDq/EF90scKpxg9O0XvF6MGAgDyc70pP5PbGkKIfwkhjgDwJwB/tmnzmhDiRCHEib169UqvgEy7wtthJzw5NQjVqOOoUZ4t/5+98w6T46rS/nurqrsnR+WZkUajnLMs2ZIsyUm25CgHbDAMmCUsLCxxMbC7LGFh4eNb2IX9YNkFEZY1YLAB2UjOlrMt27Jlyco5WpYsjTSpQ93vj+pbdSt2dU/o7pnzex49M9OVTldVt86p9wQc4UPwFmrzbFlhkeoYCz1RiT+VV1BDFqIgKNpAT3ZMhKJn1ugldZuzUJZ+8uwMljoTqZxq9ADg6w9Y87xiHimWb0mpm+KnmEMnUD2asXzzLzvwt7/Zgp0nz9uevgepTvJ7zRSA9oQKnzl68im8dtYoAPbUzeHp4epyp08vp/OMlLpYnk4TvWlOg6eiJ6dunu9K2Na5ZV4jvn7jdADGKAwgYLyCR9Mbv6BaBESZVByv2ssuj9TLbFLUwgTXue4rjILirLuSEfeDuGa/ev6geU+Ic9YRT+GGHz6DPW95BytysKkqivlZbvcYzm1tY9XKdiVyDPQ6Er6pm3KQKa65SPMVA9NzrtHzuZ6JlI6IorjusX2n2vGTTd7tup/bexrbjrV5LpORayllDp3psN0DQgVXGHPdGx1xQ9GriMkz9Nzvwevv324+7Gvn8KoYJgyr8LVxgHEUQJP0d2P6NT/uAXBDn1pEEBnQql4D16NInp8CgGOespvSNj1RkGybhWfLSjBOo0CPyD9FG+jJjiE3m7FYzUJkZ6GmzHga73SeOuLJnAsfTp235nN5tSCXUzdF2p1zALqiuBMOZUdYHrbsnMclYwaUem5dRMNSbip6/gdZNW2EaZNQBmrKjKYYcnqeU0U6dq4Lc7/2sPl3V1LHLfMaMbQq5un4lUnNWNq6krbmO5qqmIGx6VA7dmGNV4CZ/gcAX71+mmfgDli1iZlUnIaaUtdrXuMVwihpgt5U9JyOeBjHWpzHN46ew389tQ9bpRRhYZtokPNPf97uG8jIwb5BOuXTmbqpGvtq7/Z/cLHhjROmStwRT7lSNcOcs7Md/oqePPNNpPj+7eWGYyNOWU+7bjp5aPtJaCqD11QGzoFV39uEv/nfV83X9p66gNt/8rytRs4PnXunQRvL3DV6qsLcY0l0jgtdSdt3nvNh2a+eP2TrUirqO7/5oH3YvYyqGKMqdH1QjFd4CcAExthYxlgUwLsA/ElegTEm58OtBrC7H+0jCAdJRKq2Inl+KsCjaMDbGMHeoUHpPiTaZiPFgGMVb0FDuB4IBNFXFG+gJzlnZo1e2kFPOFI368pFoGffR3t3KlDNaKpzO+xeVDk6eg6piOLEOUvRE7Y6AwhVcSsCsoIhr68FeJTmcPI+bM0OWIqNM+VQPoNChdR1y5mtKTXOf6fUej9ju36dQ1MZVMY8HT/ZgW7rTNiuY0Rl5igIS9FzpG5KXTflgPq9i5t9Az0RvGRS9JzKLeCttKZMhTBwdwDC1ZuFxRlkhHGsEykdP9m0D2v+/Wl8/YE3ce0Pnnbtryzqnc43ROpA6hyYLZAVT01hZr2t6NrpxSfv2WIORL/QlXTdU37BpsyZ9rhvwC2nGXIYuZri0us6h6Jkvhf8lgc9LNFUxTNAXP/6Mew4cR5/fu0YfrLJqG18q81/ILwTPT3yxHuZ9Xs8GXyf7zhx3tZV1uv0fUIKRsV1uWhsPQBg4dg61/pKOrvBa67eQINzngTwcQAbAbwJ4Lec822Msa8yxq5Lr/Zxxtg2xtgWAJ8G8L48mUsQUCt2g6mdSKTTNucpxnMHUvS80bsaUBovxyMVJZjEjuTbHGKQU7SBntz6XDgwEVUBY+nxCpKzIJ4+Ox2XzoAavY+vGI+nPr8SY+rtdXWi3kzGqeg11ZXZUjeFIymnGwLeqZtyuljUFugFpW6mA71eas3+V0vHYkiFey6OqMsJSt0UNqekGr2qUmM7OdgJ00ZeVRhUhblUCF3nti6QbV0JWzdJTbHa81s1evZ9/++Lh/HkrlPQOXcF0X7dTsN0G/XDb3i5nq5va6wNfqjgNYevJ7bYuyxmfl/dSR0/etI7DSWeMmyTVVaZhtpSDK8yAoM2l6KXtkkaPyHX6PmlbkYcCvfbF7pt92UypYc6Z50J3RYgyrdCh3zsdE2e0OBTerium36KXtDMxIjiPYi9XVI9//MpI43zrSzGlug+nUsBu6JnPtDwsf3o2U58cEnwgGT5nIrf4ykd0xuq8L3bZiOqKjblWzz04hgUih445w9yzidyzsdxzr+Rfu0fOOd/Sv/+Sc75NM75bM75Cs75tvxaTAxmIlVbwJNlSF0YD8Coz+vgMezgo/NsWaHCwNum48WSGCZG6KNL5Je8BnqZhsYGIQ8a5jAcFYUxRFXFGJguOQtCsXGlbib8FT2hjjmDmppS9zw+54y+0XVlSKQ4Tqdnd4kAzKkUKQpcqZuychMNmbppKnq9pPrMG1OHD3g4cv6pm9bfMVPR46aaVhJRURpRbeMVwqiPmqKY6WNyaubaHz2L379iPSXrjNsDF01l0BQGhVnn0+sy//CxPUjpRnOVhppSfHn1FADA+y8x3vvXrp+GDy9rMdf3a4kv46fOdXikICZTOr72gNFhtMJDBZTxmgOXK4kUxyapc6z8cMFPOX5x/xnf8Q/yGA0vKmMa/vixJQC8G+cA9kBYVRSp66Z3QFTiaKCz+60LtsYmHR41e37I97NcXynbxGGoeaai18Oum0G1tJrqHejJiPvlyDvOQe7+pDj3fLClc277fMkD072+H1dMGoplE4Mbf8iqtji/F7qTKI9qGFVTil3fuBpTR1kPzdT055UPAkWPIIoKFodWuR2J8zMAGJ/recoubNHHIYXsGpkNJk6fuxicMaDKPe6KIPqTvAV6IYfG+mLruskNR15RDDWmO6nbWteLIMnpdHV0J32nE4n9Hz9nf2Iu6s1knIqe6K4pthWpm96Knr9DJ6cURkI0Y4n7pIhmy6UTh3o6ms6um21dCdz6o+dsQYiZusktFSqqKiiNqvYavRCKnqYYqZuAvbbo1UNG98YhFVFcNW24Edg75rAxZsxiMwM9j/3HIgo451AY8MwXVuKDS42g7sPLWrD7G1fjzsXN+MyVk8z1zUDPxxEti6rY/OUrXK9Xlmie3Qb//Ppx/OyZAwAyB3q9hbg33vfTF/HEzrcA2NMpvdJOgeDRGOIcO+9vQUVMM1Xd7cfP2YJy8WunlKIZUZi7xtL5Phz1rofOdNj+fvD143jkzbd8bbaOz21p4A21loJvC/TE3Lz037pQ+DIpej4rBAV6EanG1A8xYuVgwHVx4pei+vFfv2obGSPOuaq4H5B88ZrJ+H/vmZfxWPZAz9hfRzxpe10+NwozgttT57ux9v89l/nNEATRL2iV28GUBJJtswAApejCFHYIr3CqzwtCjw9HY5eK/ZXhxlcRRF/RP96lN+bQWABgjImhsb6PPyLM8jo2Pf0MgEowcJw6dQpxneFg59tIJaJ4Y/ubONmtATCCsgN792Ddui3YfSECoMbcx95DR1CqcgD2GVkA0H3wVaxb9yIA+5Prk4f3A7Cn2Z1wvHZo+ysAqkzn/vQ7ZwFoOLR/r229X/7iF9jVHgVQ7fl+9+/dba5/vu0c/C7X//z61yhTOd6JKwDqofIkwsbwCjh0SVf8UPM7+M2vf4mXT5cCsM+De+CP9wGow+ZXXsW6w8/g9XMxvHjcnsq68cEHANTisSeegAIOoBobHvgzUt3V2PrmTqw7awxuP5O2NYg3t29DiaoDqMC6n/8Cm06XYWplNwCjxkfv7sCJw204diFqOz/btr6OdSeeB0/VY8vr27DurRew47z7PL91/Ch0DrSnFKxbt87TBsM3Nu6B115/A+tOvoCdHvsCgFJ040+/+7X0irFdJNWF7XsOALCnwz7x0lYARkfSs2+fgNd92NtcMeQc1p8wxj987w9PYe+I8zjbPgRCW+aJLsDjKe2JM+fhd0/98c8PAqjBqeNH4fUeThw5gN/8z1YAQ/Gr5w+h/eAbmFVt1JZ1dtUDUHD/lmPm+g89tBG7yhJQMMR2b8okujo97RR84Q9bfZfJvH3mHfz5gb9AfC+cOnbEfA9dCR0//dk6KAx442Q5kslSbN68GUAFDh06hLa4il/98pcQ13lyRTd2XLC//5MnjsN53cW+/Thx7Cj+eN9OZPp8AMAL2/Z57t+L115/A0CZ57L/c/8L5n5e3PwKgHI8+8wzaO8oh3yez735LO7Z95Rja7e6d/LIQYh7+y8PPYx9z8Zx/FQteFsS69YZqUwHj1RBnOtXXt6M0+djON6lwZ3nQBBEvohUvQY9UYVURzMAYJayDxrTqT4vBA3nR+CFoUdREjmGrsSofJtDDFLymboZdmisORCWJbtxxVCjK6Xc9Y7zdGoVAI1xJLn9SXRM4el9Wa+NK48jobu7ygHAotoOjC/3riXSFPcGUcdrtVFHswueVhQd/gtjwS6NKgW2QQ07xLlIpfcW87DRj4vr7Klfo0oMZcXrcJpivAfxfpzvGzDOP2Cc/2R6PU0BIgqH7NvqPLMzpzBu3qCdKQVPnS7HLw/XSMuN69GlKzgV12yvG8c17gXAW9HTGAf3HbVu3xdgvZ9sE2QrNI7OlPso8sSCkiyuWU/QpHtqy7kSPPRWufm+AO9rCsDTfoEQZ/22jSm67bN3Oh6c7qOkr1ZAtrLn5zCIUkVHTPGY0ciZ7XpGFI61o9owrtxQqeX7hzFu3itmKqe07cV1Hbh6+Hnb/kPMOnehMPtDrSDOJMKnTonP7dL6dtw1xj5cXT7X4np62a6GtEu+F8T+unVm+26S96SkzyWnII8gCgelA2rFrrSaZ/xvPJcZjVhe1cfn0bDioDvdvGZEtfPhGEH0HwXfjEUeCDtp9HDcsupSAMCChRcBAKKahtq6elRWVmHcuBbUVldizNhxqB86FIta6vBXS8fix5+8Aa2trbj6qqvM/TY3NaKmrh5No93FxHNmzUBraytaW1sxo8Gu3EybOhWfvdL+JGvipCm2v+9Ye53t75IyEMHVhAAAIABJREFU4yn69Kn29VpbW3HF5Zf7vvfZM6ebvw+td3eqE9x6621obW3FtdcZo5aG19f4rutk7uyZLptaW1uxcOEC17rvveN2lEYjmDx1mnG8q690rXPL2psAAEuWLsVFiy8BANx+680YMbQOwxtGm/tfc/31GW2bM3s2LkrbMW2Jce0qyixFtL62FjMc5xQAFs6fh9bWVtRWVaC5ZTxaW1uxYsVK13pTJo7HyFGjMGzYUNMur3+CceMnorW1FcuXr/C0t6qq2nO7qS1NiFW4r8moRuvemzGlf9Jgrlix3Pb32ehw298Nw7xVJOGAP/ypZSh1pGheutK4h6dN9n7Cu3DOTNv5WLVskXmOSktKXOtfd+0atLa2oqLUX6kaLn0egjrSCt5zyTi88OWrXK+XlpVj5WVWuu20yRPw3U/cjvdeNgcAsPbW29Ha2oqpU6chGoman4uRDY2oqa7G+1utZojXrlmN5Usutu1/TFNjRtuctDSPwV2td4Za93wyfKA3fqKRhrz0onlove0m27IxTdYztinTZgAAli9bhtJSuwL4rltv8f18yMybNc38fcmy5cZ6agyzp081t2tssu7/RRctxLBhNPCbIAqJSNUbYCxldtsEjEYse/WROIvKPFpWHOyIT8e8zi7Eq3bCt06IIPqYfAZ62Q6NBWA1PxD1JprCzPEKcjMWXecojaj40uqp5vgD+Qm1pjIkU95z52TH8fcfvRjP332Z7fiLx9mdYWejlGpHwxZRj+bsFGgY5f9e5Todv6YOgFSjl66VClvvpTCjlbv3MvfxopqSPm/+DU6EnSmd22oGSyM51uil93fXz42UzwqpHlJVmGc9orh+co2e15dsTFOgc+5bR+VE1Br51Tr5ta6vq4h61uh127pE9uw/gW+vnZl5JbhnOTrn2vmNSACA0oiKCcMrccMcu/Aez9CMxXk/yjWOnkqrdP38kGv0gupXBcOrSsx5mjIJnds6RIpjimBW1NLxdKgramp13T0w3bgf7TZnaqriR9B7aq73Tr/MxG9eOmTa5Ny//Le4nsa4g/B2ycg6eSKpg3OO9ngS5THr/MifFzFHjyCIwkGr2oJU91DoXSLtkGMuDUoPzSnU4pILwPlYB5RY5lmnBNEX5DPQyzg01gvh+AtnWxFdGdNd8CKqgkR6YLozOJIdek1VkNB1T+dcdjiimmI2khDHVx2jDtbObcSXV0/Bc3evxA/vmIvhVSW2Fv2JlJG65hVUBbk28kgFpxPUXF9mBo7CcRaBlV9DDSdPfm6FbzdPr7gyqimIqAri6aDEq3OmaMmuc6vrZiyioiSi2hpQZJqjB1jjFWTkoEFVmOcoBPGeYpqC7mQK/7lpL9Z7DJQuiajQ9fDOeHfaZr+ugH7NOKtLIxkDvaDmHGGo8ugG64UzEHHOtQu6d8SyLoetmcYriOD8R++ZCyB4tABgXT+/MReAveFQ0EOQCcMq8L3bZqP14mbP5cmUbuu6KYa+i5+2QfdSqqbO3UmGCmOuQDpkXGQj03iB2vJwNXlOxG5VhbkeOkWk8ynuS6/z6vmwygO5K3I8padHetjvL90V6IXaNUEQ/cDJ9pNQy/an0zaND+dYdgJ17AI1YsmCuvNNUDmHVvVavk0hBil5C/T8hsZm2k4EEsmU5YwYc/TSip7oupket+C1LWOGapDSvVt5Ox0c+Sm22FamJKLig0tbMLK6FKtnjgRgDRcHjKBUYcxTOQoKMuQgzLlaSUTFP99opFiJ1ugJM9DLnM41eUQlmurKEPGZz+fVDVRNd0M0lS0PFUq8R1fXzfR4hWNnOzH27gew5fDZjDZqIQI9T0Uvfb2imoKuhI5/fnCHZ6CnMOOcBQUKMmJ8hZ8j7hzKLqgujXiqgHKgN3d0bSgb/Ihq4d6DS9HrdgZ6/vdORcwj+IGlAJVmUPSWTxpmrC8F+V4PWixFT7FtLyN3+HRevipJ9e1O6rhhToOvcp1IcSSloKTET9Fz1K2JB0syXope2HtLJtMYubq0Mjl5RCWummal3i5uydy8RdgUcXxu5OwB8fk2Pst2YzIpeksnDAEA21zLeFI3x2TI34vy+1QZy1AtSxBEf7LhwAYwxpFId9sErPo8UvTCszc5Hpd0diFW/Sqyr/AniJ6T1xo9r6GxmRCBhHCcVYVJc/SEiuMT6DHxk0FTFCRT3nOlnM6ZHNgpjLlUMK9W6HIKXDJl2Obl9AWJSfL6ztXiSd2WJglYDlppJLOiJ7bNRtEDjCf64jheqpzYLqVztMdTpnpQGjUUvUfePAnOgV8+fzCUjUGKk5+iF0kbEVEUPL3nbddyQSLF0RFP+SpRTjLN0fMbDehM5TX3l9QR1RS88U9X4ZLxQ0LZ4EdUDfce/EYgCMIoegvG2utFzTl6PvsW40fMkQkZ5tsJJVukZzrHlwDBit7QSqvzpVN9dJJI6dhxwmqgIgJhL0WPQVb04ApMVIW5xj54PcjJJCBnmiMnFD2dc/z4zvn4aet8LJ801JVS7ofCmOsBj2yTlbrpblaVKdD77JWTUF0aQUq3GvDEkzq+98guAPbvRfl9Kkrm4fMEQfQff9n/F6Q6G8DjVu3sPGUX2ngZ9nDqIBmW13gLrr7QDkTaoJYeyrc5xCCk4JuxOJEDCcAa6isCu5KIiu5EyjN1UzhdKmNmwOLlUjm3Y8ya66V4KHoRDzVFVjcSug7G3OoU4HYWZYKaTHRLgZ5wmETg5XQ2AWCMo67HCvTCK3qA4egJJc8z0JNsOn62EyOqSsAYM2v0TMc5REmapjC0SAOsAbu6w+BdxyXe29vt3YH7T+o6uhIpXyXKSSJDoOeHX6DXGU9CUxgqYppnwP25qyZ5bOWNnFL37Zutej3nZcw0Y9HZaEVGBHofuKQZ88ZYCqRVo+cdJFbEjPevpIN+WclMeKnC6esnPnNegZ4csDo/V3ctsYbcd2cIKpM6x6+eP4i6dPDkTN3sMBU9e00e91D0FBauRq8yQ2p1xkAvPctTrLZy8nCse/9Cl1rrh1fqphx8x5P2bAmZTKmbqsKMbAnOzaCwO5nC1qPnAAALmq2HBPKuuceDOYIg8sPBtoPYdnqbrQkLAMxRduNVfTx48bmOeWOr3oKVHZ1QdQVa9ZZ8m0MMQoru0yoCCeEgqmaNnhGclESMdD1jgLp3oGfUyzEkdR6YOmZ7TbWCRGeNntdTbjlFyUgrzV7Rk+1wWmnU/dkDPeGgeSmMMxvtXR/FuYj4BJN+TpemKqayFZS6mdI5jp7tREOt0SWzJKKiI54y54ftSw98vmS8vwqhqQqunDYC279qdUuUzdI591b00u//pGPYvZNkSEXvwU8sRcuQcjyz5zTOdyV8B6b7NWPxC/Te6Ui4ghqZTEOzbeumz0NUVbBq+gjz9RJH4OGl6F0x1Ur/CwoERZDNGENzvRWAd2dI3RxeZSlsolmSta1bcbNqLFXT5vdf0ozbF1q9m2Q7nffq7Qub8OM7jaHemRS9lM5xviuJqSONeZDO1M2uuGjGYtx78oMm58MQTXHX6Hl9vCpLguspMz1IEAG1MwgLe794fRfZAj15YLpjW78HQAJNNR5opXSrydHz+87g8JkOrJo2AqOlB06y/ToHfLLICYLoZx7c/yAYGJJt1kPDSnRgEjuCV3Sqz8uGNpTjeGoEZrRHoFVuBdCzenyCyJai+6/VbMaSdkY0VdToGemRpemmH6n0317bitTNREr3fHruFeSIoIsx5lb0PBwsp9Mb01TPp+GBqZsBjtv33zVHCqqM10STFNkJFs6537nwV/S8jxuVUjeTXs1YpBq9o+90orHGCPSGV5XgQncSb1+wq2x3Xz0F33/XbNd+AOucl0U1rEnXPsrBJYd3YNJUZziT7Wkn3U9Biad0dMSTgZ0mAWDqqCocOtMBAPjjlmNmTaSToGYsXpzrTJjv0eseCmpG4rduytFF1Bl4eKm9n77CqrcI6nQpB8TyZ0Ck13pdi6UThmCMFBTGIqoZ3KV07qnoOVM3NYXhH6+dhm/eZDkd8n0rBy03zW0AYwyXTzGCVznoDULUJopjetXoMcD8YKS4lcYpDu9Vo+d1S/jVQX7ysgnmsYIYUV3iue9owLWTURzdQgE4VFbdXC9bRF1tMsXNmtWn97yNdzoSrgcq8ndvSidFjyAKAc45NuzfgHnD54EnrfFSs5S9UBjHy5zq87Lldd6CG9vPQtHaoZbvybc5xCCj6AI953gFK3UTZupml0jddDgOTHLIIunxCl51VV7Km2ambrpTML2epJc7goeSiOJdr5ND6uYNs0dh8bh6s6OfWaMnFL20w33LPKMbqLD7Skm5EefGLxXLv0bPasYS93DSxVP57mQKJ9q6TEWvqc74ueetC679Da9yz1ID7Nfh32+fg3FDy21qB+fewZBz9uHHV9oHu1aWaGiuL0MyxdEZMnXzi9cY51Hn3H+8QkAzFj9Uxf86+NVPeiHuQWfKslPB8wrkWoZagVjQMeW0WXm9Hz25F4Bb7fny6in4xQcWuuwU6pFfrZ7mUDm9HkbIn235/X7lumnmay9+6TJ85+ZZrm29aKwtg6Yw8yGBCEo64tJ4BWZ9WuXUTREUKh6Knhd+dZDimEGpmw99ahlGpgM95+0WtiOm1/ebXWW1Ar1MQad734qh6HF3/bPzcyYvz5SuShBE/7Dn7B7sO7cPq5pX2V6fy3ZD5wxb9HF5sqx4eU0fh9Vdp4BUFFrV1nybQwwyijDQM36mpGYsum41YxGBnlfqpmqqcobzGLbrpnHcdOqm4lGj5+FgeSl6XoGb7BsvHFuHpROGmK/5BXrCFks94zjZ1oWDadVJKCsc9k6j//ne+WbQIQIyLWTXTeHMRlQFiaRxzoSqKtf/iXN37GwndA40ikCv1lhntyvQY76Bnr3rKLMFmeL9OYPsYZUxM/j7+QcW4nNXTUKtY36a0VBHQWcihUSKB9alCW6Zbwy+jid1/66bfopeWeZAL1MwIxRNmcc/u9z8XQ545UvnDPTkQOSuJWPxmw8tsgV/QaqKHKB43ZsjHNdRU9zKUVSzAj2v8RwAoIrUTfOecx9L/oy+Z9EYa//SeRxWWRJaFW0eUo7X/vFKs/tpeUyDpjD8zwuHwNNdfRm8u26Kz7rqUaPnhd+cS3GtglI3m2rLzPXcil74Gj1zG6mOTiDX6PmlI/shFL2UR1q8M8CVl+uk6BFEQfDQwYegMAWXjbnM9vo8ZRd28UZcQG5zPAczr+stiHFgyIURiFRuA6VvEv1J0QV6wkkRXRuFMyIGGJdEVHSlnXGnL2oFawwRhSGe0vHC/jNY1FLneQz7tsZPr6YqXk66M02pJKL4NGOxWDFpGH5510VmwODVhELYIL+f+149iov++VH826NG62Ph8Ml+ltAi5DQzw3bLAjGuQd634M2vGU/3ymMaLqRb8os0yvV/s8S13aEznQCAhhrjPwWhlDhTNw1FLwYvnLWQoq5SwDk3gwHACHCfk4bbXzpxKD62Yrwr0BINI8Sw8DBdN0VqZdwn3RfwT92siGq+qbAi0JYDlI+tGGfaCQCXTxmGf73Nnd5qKjuwp37KAaLzvcnH+fs1U3GRoyV/kK9t73hqvzY3zB6FGY12JdUr9Vh0xQWAVPr++evl9ifEYcYrCDvvvnoy/mqp1Xwlm7pGp13y+yuPabhpbgPePN6G9njKrNETnyNdt34XgVcipYdS9PwCPXENg2KrqKaYx3Peh14D4b0Qn9ED31qNXd+4GsMqY76pm9kKbYoU6LkUvYi/opfi/lkEBEH0D5xzbDywEfOHz8eQUqsTNIOOOcoeqs/LkW28GQmuYl67BqZ2Uvom0a8UXaDnDECMznCi4YmROhVP6kjq7vlo8ngFNWAYeZCiJ+r7MuGs+4ppqss5Buyqo9NHbay1npzJDpczWPvvp/fbtjMdRnDzsb/83uWfQi0piSi446LR5j78fK7asgje6YgDsBQZOcgQ+z2SVhdF6matj6qlqcy3Rs6pGom6SgHndsde5+5OqwBQ5Wh+oTDD5nOdRsAaJnVTHg3gl7rp10pUUZgrlVfccuKWkFWrz101GQe+tdo8l5UlEc8aPvm1qM+4AWeg59dMQyiGQb52hVRb5lTZhlS4g/USD4VJVvTEeXQqUWbwm37def0MOy2lWn6/ThU/LF71haJBS9K855inord2rqH2VpZo7ho9j1sipimeiqhcZ+mHPDvSudqyCUNsc/WC9iETURVboBc3Uzcz7soFg/GgwStbIqhGjxQ9gsg/u8/uxv5z+3FV81W218ezY6hiHTQoPUe6EcUu3oirO94GT8UQqXo93yYRg4iiC/ScTooxR4/bUjcBo7bG6dRaQU5wLZL3YHNxPCu1LAin02Ioeu715LWcgWBTrXeKhPk+fDwxkQo5oqrEdKac25iKXvqYztjFL5atK4/i+Lku3P2HrWYKp01NSu9XpJEK1Ykxhh1fWwUnIk3tE5e5/wNxO6TMNteMg9uai/gFYF7jMiIqQ1tneEVPSauA8aQeuhnLU59fgUc/cykAdwOOmnQKrTj/uQzWlreRrwGzKXqZZyoCRg3k/m9eE6joyfty2luWVqmEGgl4d/iMaYprHqHfkHER0FR51Dj2tvrjlXKpSSoudzww0bnVdfOTl03A61+5EjVlUURUltE2RWGe6cIieBafWb9rYaVu2m84xhjed3Fz8MHh/m4yRl5IqZspKXUz497siIctRkdj+zJ3oGffLlNHT4Ig+paNBzYaaZuj7WmbcxUalN5TXtPHYb6yD8nzU6FVbgOQzLgNQfQGRRfoealvJ9u60R5P2RyojnjKFbDJapasSDhrYjwVPVvHzswOyaWThmLpBCv1wU/Rk010xo9+tV1mUxkfx+iqaSPw/XfNxt9ePtF0psShFce2ZsDrDPR89i3Sw/73xUN463yXW1FJ/5rSOYZVxmzOfklExfsWW/VUC5vr0JDuyvnpKybaRlIAbkVPVZjZHAMwFA3bCAofr9TVcTRdoydSN8MMmAcMxSWR0uExPjB9fLsBTXVlGDe0AoC7PknUDVp1ox5pvVn4vX5jEbyCWIUZ3Sntx3LX0zmpCKjRE9fuc1dNDrQpoip4avfb+PGTe82urfJ6/3rbLOkhhPHTS9HLVbnzw6sbqVBxjc6g3Ja6mdK51XVTYaaNLESdnsoYSjyuizgP4jP72GeWe14/cV687ne/7wTbOh6p53IzFtHUSVEYLpsyLOP+ZMRMyKTHh8T50EH+vKQ85hISBNF/cM7x0IGHsGDEAtSX2lP657FdOMMrsJ+H62JMuHmNj0M160D9+SYwtYvSN4l+o+gCPacqJjs2jNmbTXipf4A7/TJUoMcsh1xe/tTnV3jaeenEofjlXReZdXYlEQWzm2o81pRTN8N5OlaNnnuZmq6RuX52A6KaIqkD9pRN4SgLJciZZuXn9NeVW073q4fOIuIIXo2AwfhdpG3K/NP1083fP3XFRNtx/K6XIKIq2H68zfyb88yt6IVNMkbqJkNbeixAGEUPsNIOUz5NRIJMcQYrNekgPoxjHqYhhldqJ+Cdlrrvm6vxf2/1HmkRuhmL43hlHnVnXoqeqO/8t0d3W4qe9Jm9YbYVgIp0Qr9a1d7EKygVD0GSaUWPAebHlfMgxS34a1X1UfTGDzMeCoiHIWOHlNvGQ4jUamGXs8kQEC4Adt4qQqkWxKUavW/fPBPPfGFlxn0CwO8/ejFqyqJQGcPjO0+5lgelbopZowRB5Idd7+zCgbYDrrRNwFD0jPo8+pDmiqhvvKizGzxVQumbRL/R9x5ULxNUTyfGKwj8HDHh6AucKX9BzVgUZnfORZMRP6w0NBXjh1Xgzx9fgmt/8LRrv8a6hgf24CeW2mrRAHsQYdqSobkLYAUJzho9U9FT7OliXnbJyM7l9uNtnkGSyhiSnJtqnR9OFcUZPDiDF6eKxBEcXAlcTXkUe6AfNtCLqAriKW7WUDXUlOLo2U5zuV9KJ2AFdoKKdOAXFNyL2yzMe3Tu55Z5jfjdy0c81bBckdNPncdzqrGAd6B3+oJR31lXETVn6EV90k67E7rvfsz1e8nx8FLhIqaip5uBnTiazrnvkY19JWyvfWhZCw6ebsfGbSehKMzznqsvj+HAt1bbXhOf1+WThprNkkZWl+Jr10/DFVPdT9fDBEvu1E17jZ74XXQRbagpxa8/eJE5l9KPpvSDHb972vl5l5+XVJdGqEaPIPLIxgMboTLVlbZZjQsYrxzDHxJLfLYkwrCHj8JZXo6FbDcePD8VWuV24HgSReiGE0VGESp6zrQje9qg7BT6qSWKwmxBRTapm6rCskobEzYIJ8erXsw6rvFz6qgqzPJU/9K2SN1DnThrD8VbM7tuKuJYTkXPvh8/B9rZMdBLSQpS9GScKoo1PNz7/TkDQc45xmQItAG3Y+tM3Q0KJGTEDLiUbqh78ngDIDggq3HUmYnGJkG1oj0JYr550ww88IklGFXjPbrCD78UUMB+7Z3NWLxqAb32dbrd6LpaVxaVFD3v8y/qxoIUMr/ZhZlY/zdL8O211gB2r2OI95hID/9msNJbde6ven9MmtsorJs2qsq8f1XHAymBV6Bjzbu023fn4mZzcLpMmDo3r9pXz2Ys0iEvHj8EV0wNbvSiKv7fS4BbfRcPl95/STNuXziaAj2CyBOcczx00EjbrCuxdyGfk67Pe4UGpfcIDgWb9YmYr+xCom2mkb5ZQembRN9TdIGel9MuYLA7UE6HQziWzjo7l6Ln2YzFv5Yq0F5J0TP+ti+X9xbW0XGqczJOB1L3UfSEXX6Bhl8s66wb7Ey4n/ILpabRp5mMwKmiiGtSmVahvJpGyHAO1JZH8cIX7U8gnXipwLKiF6YlPmAEd9uOncPpC91QHcGisMcPZ+t70YVTvkf/Ze0M/P6ji0PZIvja9dPwmw8tAgBMb6jCP147FYARFE8bVe2b0unHrQuaMKTCu01/0HgFZ7MZwDuAFvdGXXnUrNHzG4nQlRA1fP71bNm+P8H0hmqMlILg3lT07pTm+slpt/IDmjCzGwHr+yL0jLwwgZ5jHTFTVCCPV8iGTIGe82GSODW3zm8ytqE4jyDyws53duJg20HftM0kV/Ca3uKxJZENL+uTMF45hqr24Ub6ZiWlbxJ9T9Fpxk41TQ7YFGZv6e4MykTQoyrM5iA6a668umrKqZvZIOwTwYTTeZL/DFJ37NswX1tKNGegZ99GNX8K+8INTBdMG1WN3354MY6f68Qn79liPv1fM3OkbXA6ADRmSt10OK/ivFeVaDjTHnelrzqDC6HmZHL2nW+FMSAiHTvMuAzACEh2nDiPHSfOozKmmfWawkkOGtNQ7VD0RNAkO923LRhtW8dM3QwIIO9c3Gz+vv5vlrqWZxsIxTQVX7xmCj7929dcy+QREc5bNayiZ64f08zz5jUQHbAUPa/9fOTScYgnddy+cLRrWVjk6+7XOAYQip4Rh4hNchkHINZWGHPdK1NHVqHCoxZRHCPsfMAwNjm/Q53nX3xnZNsJNlOgN72hynEc6/sYyD6wJAiid/BL2wSAeWw33uSj0YnsskMINy+lu5bOV/biqfPToFW+AZxIArzoXHGiiCg6Rc/5NFp2KpjDgXL6RsJxG1YZs23nSt0MUPTCPDH32k4oBs6t5fS8sI6O2XXTw6FyOpBWjZ5QJNPHcnQ2dNvtf/yFY+tQX26fm/aDO+baOi4C8Ewtk3E616Kxi1D05AYRgDu4EAGQX6AgcCl6jCEivcGwDm1Ek66VI80UAO5JK2teuGr00oFemDTg3JITDXIZIO53H8qqndPusIre7z96MQCj/k4o6X7n/+Mrx6O+PIoFzXWuZeUxDXdfM8U8hleNYCai0vX06rqpmambQtFjVtdNsztLOBizxi6oivtz+uAnl3qeB9XjPgs+TuZ13E2PvO+RbAMvETh7pRy/9KXLMbLa/uDHbBRlHi+rwxEE0QuIIekXjbwItSW1tmUqUpil7KVB6b3EVt6Cbq5hnpm+2Q21fFe+zSIGOEUX6Dl9EtkZcTZjcSo1TXVl+M7NM/Ef757rGq9wzQyrsUGmgenZIFYXip5TKbONVwjydDxSwGRbWtPzs8KmbrrGK7jsDn6fToXKC68h2jJ+89NEl0V5thfgTrEV7y2TauV1z8jvO6ySKguMZj1heucfXT7OHKXgRV25PR3SdPSziOKC6jb9kIPTsDgv/ajqEgytjNkUPef9UeYxosIrJXbemFrMGV2D7mTKfMDip6jOG1OHl//+Ct8xIzKbv3wFtn/VnXYUhF3RcweKUSl1UyDetp4hzvv+u2bjH9ZMtb0mf25Dp24KRS9s6maIaMn5HdYZ957nlKui53xwBnir3dyRbUCKHkH0PzvO7MDh84dx5ZgrXcsmscOoYF00P6+X6EYUW3kLFig7kWofD54qRaRya77NIgY4xRfouepLZEXPrhJ5PaW/ZX4T6itiNicvqXP88I65mDbKSC3ybsZiHSMbkin7UGjnrm2pmyEdK0sZsNZfM3MkALdzLdSQSycNtW3jbMbidww/wgR6zuDGifP6CJvE0OfpDdW25U4HUvyVMdBz1nU6mvGEdWjl2WDC+Rf3X6ZdXD19JJaMt+YqigcNqaC8zDRClf3dhxfjkU9fGspW6zg9V/RuWzAaT31+hU3FE7811JRiYXMdhle7g3q/eXIxzejyKD4buQyLd1IaVUMPhxfI3x3e4xWM15LpZiwyPMPct+tnN+ADS8batjIDmmxq9HyasWRaPwjn+T7fZQV6EUdzq2wwAz2Pe7rE4/w6H0JRnEcQ/U9Q2uZcsxELKXq9xWZ9EmawfYghhcT5aUb3TZbIvCFB5EjRBXru1E1790zZuQxKW9Mcip48Hy9I0cvWKRUNJ3xr9HJI3fyrpS2u9YVdzi6Uc0bXYvc3rsbSCUagJzubQFDqZgZFL4TKkulYfSc/AAAgAElEQVRcOa+PcGavnDocB7612tXMRTTymDIyXesTspbINV6BwZa6GbZGLy4FemIGn9xJMYiopuCfrp9m/i3eq5f6IRDXSqwR1RRUZTlTrjcCPWc3W8M24+eyiUPw248sDmyY4qQkoqI7qVuKXkhFtbeRz413jZ5hVzylA6IZi9x1M4vcTQbpAY2UYv7dW2bh326f47udMDGsohfm68l5r7Z1Wk6GfE6yVdjEsb3uaWfHXMCqBTTTyqkbC0H0KyJtc9HIRagpcWeMzFF24xSvxhE+NA/WDUxe0iciylKYyfYhaaZv7s63WcQApugCPWdtkOwjJnXd5hD5tW0H4GjGYngcPCBwyDW9SNSZWYpejqmbaX54x1wMqypJr2+9PrupBv+ydga+np61JWN33tLHMlM3/ZqxBNtR6TEgO1tc11JhiGmKb9qoUNEunzIMgHv2nx/O/Tmb8YQN3hNJ96B0ESiHamsvrSPu06Bh6F57zPZBgxxMf+PG6QFrWgSpzuZraeuCLoFf/WFMU9CdSCGRfgjSG4peLsgzBr2uX8Sm6BnnQQ5mwnwViFmS1aURW32sUPQWjavHdbNG+W6fdTOWEOfSaXebpOjJ35/ZXhdxDuVA76vXT8POr6/yXH9CekC8mClINXoE0b9sP7MdRy4c8ey2CRiNWIy0Tfpw9hYiDdZI3xwHniyj4elEn1J0rX6CGgkkU9yeuhmk6GXZjEUElNmmF4n5VMKZcW7vpcp5ISyUV7GNlmDM1bXRC3fqpvcxMwUu2cwSDIuWDvT8EOqoaGQStrzNHVzbUzfDpszGU+5ALxulV7bDVPTCBKvSKn6BuR/iOFNHVuHdF43JsLaB89pXl7lTcMN0BPUjpqUVvZSo0es/J+ILV0/GsfSQ+xHVJfjX22bh7fNxz3Xt4xXSc/TSDk/YrpufuXIiZjXWYOmEIVj/+jEA6UHkaYU/kuG9i89Zb3bddN6rF7rl1M3cFT2B/H1qPLzxfuD2vXfNxmuHz5kPrsTxbp7XiP9zyyywf8np8ARBhGTjgY3QmIaVo1e6F144hWblJH6d8FhG5MxZVGK33oD5yk4gdT0S56chUvWakb7JM2dKEUS2FF2g5/SLZCcx4Qz0AuajyQ6zs8mH93FzS90UgZ5IdXQGSJkUvde/ciUUxnDHT54PtX4mnMqkX9piT33vsF0CZTSVBaqwInWzTAR60mW7fvYorJg0zHM7z9TNHJqxOLuAAnJr+Mzby6da3KcesaOJGUxJkV62QZFZCxjiHhfIh/jAJWNxh8cIA7FKLgPLhaKXzNCMpS/4yKXjbH/fOKfRd11xruMp3VT0rGYs4RS9mKZidbp+1soYAIZXxRDVFPNe9sMcmN6bqZsBK8kBZa6XRb7XgoLFypIIlkyw6lbF8UjZI4i+h3OOhw48hItGXYTqWLV7hSMvAgA1YukDXtInYrX6Ahh0JM/PQLT2JWjlu5C8MC3zxgSRJUUY6DnqhySvIKnrtgAusEYvQNELOm6uT7lr06qIKy1O+t1LSRTpZWKJrLZYNoW3Q27xDvgHZD3pgPfYZy5FVUCzlm+vnYnn9512va4pSuA1M+fVpYNBOcj4/rv865y8xivIwUXYQEMEmvZ9p3+GuAiygy2Uk6DUTbOzZw5ppuZxtCyUwzTy+XrPotGBNau5KHolERVdBVCjlwmRxphMcYhpCsLSLOJmE1OVVxiunTkKC5rrTHXaD3Hqe7MZi3OdliHl2Pd2OwBH6maO3wG6rUNw+O1Yjg/TCILInu2nt+PohaP48MwPe69w+EXEuYo3+Nj+NWwQ8LI+CXdoj2MCO4pd7eOgJ8ugVW2lQI/oE4ou0HM6AU5FTyaogYHsjCQdA9O9/JuePm2uSQc+udboWambUqCXXj+TsygjnDexrV+KZhgf76FPLfN0QFsCxgwAwK0LmnDrgia3bQoLVGFF10txXfUANUzGK11Wy6G7oGfqpqnoZedgi4A26CHD5VOG48PLWvBhSYXKVtETx9GzUfSkS+CbwmsqW1mZA0BW9PJbo5cJca4TpqLHHIpednabA8LTqcPOZkNemKmbYRW9LB84AMDvPrIY877+CABn183croucIZHNOfJ6mEUQRN+w8cBGaIpP2iYAHH4R2/hYdCO4ezaRPS/xSQCMOr1dqSYkz09HpGoLpW8SfULRNWMJGpiedDjifrUhgD3QEwFDUBqaNXC8Z4qec2t5f+HqvKzfhfOeTaDnN/h9tKNbZ5gOeBOHV2LskPLQx85ERUwzh6V7EU8H8tEsFSDnNVMUu0Mb9pp6BWVijl4Y9cNWoxdCadNUBXdfM8U2piJrRS+bWsA0dtXYex1T0fP4zPz+oxfjv98333f/sYhi77pZoIGeuEZWjR4AqQlN1laH7BIrI+6rsPd8Ll036ytiaK4vS28vfR/lqLTqIVM3nfh9NxEE0buIbpuLRy72TttMJYBjr9Kg9D7iEB+Gt3iNUacHINk2A0yNQ6vYmWfLiIFI0Sl6Qc1MnLV2wYqe9btT0fOip06IGALuGpguHyPLjnlClVowti60HWJ72dm8768vdgV6+fC9v3D1ZHQlUr7LRSDfUFOGhppSfOW6cGkO7ho9ltPYAS/EPRbmtrCnbhq/Z6O0GcfJNtAz1k96pJ36YWvy4xPOiFe94sd5Y2oD9x/TVCR1btavFqqiZw1MT4ezTOq6GbJGT8ZLlc9E1opemAcOHrsqTc8glOtQc/2uS+q5pW6aadCFeTsQxIDhjbffwLH2Y/jr2X/tvcKJrUCyEy9ToNdHMGzWJ2I+2wUASHW0QE+WQ6vciuT5cN2xCSIsRafoOR1dOQUv4VL0/N+erIKF8bWt1ughjPTASvFz7je7VCl5/ZHVpfjdRxbjX9bODG+HR63hnNG1qK+wD7zui66amWiqK8OE4ZW+y5dNNGb5jKguwTNfWIkrpg4PtV9XjZ5jYHpY/u32OZjvCGKEAx4uddP6XdybuaQ+ZoMIaMOOogDsdvpmbqZfD6ox9EPMlGxPd3vMphnLf79vPr50zZSsj5kLcuomkK7Rk0YIZJvaKM5VNoFtfwxMB4A7Fhqp1KPS4yD81vPiGzdOx01zGsy/nV03w+Kc8UkQRN8g0jZXjF7hvcJhoxELKXp9x2Z9EpqUUxiB0wBUJM9Pg1b5JsC8u0ATRK4UXaDnxJ66GV7RmzKyCt+9ZZbnMi8Vw+lAPXf3SrzwxcuyMdVzP/JfQU/QhT/t3H5Bc51rmHUQItUu2yHjhcDdV0/GM19YiaGVscwrSzhPK2MsY1t7L66bNQr3fvRi22tCMQuVduvRjCWbbpi5kMtx7GM7gtfJxXqRUt3ebai32QQDl00Zjr9a1pLDUbNHVYyavGR6YDpg/7xmewfJzVjC22D8DB/ohdinx0V9z6Ix2P2NqzGiusR8LWxK7bsvGoP/e9ts8+9UrjV6pqJXgF8+BDFA0LmOjQc34pJRl6AqWuW90pEXgapGnEB9/xo3iHhJN+r05iuGqpdsmwmmUPom0fsUf6AnOQXZKHoAcLlDEbpy6ggAwLAqdyAhfB4RcI2sLsXwqhLXek6GOYISV9fQEE61lx09JfN+Cs/Z0lTFHECdDe6um9nPo/MjqoUf9uw1MD0bpS0XhFiWzXHk0+XnqIu5kCIlORuq042J3r7QDaBwa/SMBwIK4ikODm5rxmIsz25/5niFHOrWejd1070OS6czy9ci15TaVI5dN3MdYUMQRHheP/U6TrSf8B2SDs6BA08Doxf1r2GDjDf5aHTwGOYpIn1zLPRkBTQank70MkVXo+dE9gkSWdToAe7RAh9fMR53LhqD2nJ3lynhfGSrwDz1dytsdUzMYVK2c/F662l3JlVhIPlaXsF1LnP+BAuaa81rJZpkhFFpPAem97GiJ9Iic1b0fNa5fMpwfHn1FNzuMWMvE0I1OvxOh2FjgY5XAIzviGRKt8Yr2EzNMnUz/TObZwxmoBdW0QvTdTPgO0SeC5lr46ncm7EYP0nQI4i+Y8OBDYgqUaxo8knbfHsXcOEk0HIpsLl/bRtMJKHhVX08FihCwVORbJuOSM3LAOsGeHaZSwThR9ErerJXkE3XTcCdDqUozDPIMw5jHCdbvzymqbbUyiDHJ1SNXnaHdxFWVRhI6VOuZixKz5qx/O4jF+OeDy0GkGWNnnTIaA61c7lQHjPuvVvnu8dZ+GEb4eHzvhSF4YNLW1CeRcdXwahqQ5U98k4ngP4dmJ4tmqqku26mB6ZLn8DsFT3jWucS/IRX9Lxf/9fbZplqXVAwKNbpiaqWezMWd/0wQRC9R0pP4aEDD2FJwxJURH3GIO3fZPwcu6z/DBukbOaTMIUdRDmM/wuN9M2EUatHEL1E4XpYObBq+gjb35mco2xSxqzUzZ455u5mLNbvYWKP3poxlblGb+A4W67xCqz30gVFwBgmHc87dbNXzPAlpqnY+fVV+LtVk0NvE6YZS08YXm08qTx8xlD0CjlVL6IqSOjp1E04Ujez3Jel6GVTo8dMO8LgG5hLsyODjq+KcSE9uCZ6zjV6afsG0HcPQRQSr771Kk51nsKqsav8V9r/JFAzGqht7je7Biub9YlQGcccZQ8AINXZDD1RRembRK9S/IFeOvBqvbgZdy4aY1uUqUYvGydEOD49dczdA9OZ5+9ORBOVnvrEYRWogeRr9eV4BbGfoBmM8nGd2/V16iZgBHvZNACx35N9Y8+Qiqg5XqFQa/QAI3UzkZQUvSy75Mrk1HXTTBEOd78G7ZvZJgH6bZ/+2YMLP7/Z6kybzTmymrHkfGiCIALYcGADStQSXNp4qfcKug7sf4rUvH7iVX08UpxJ6ZsKkudnQCvfBShdebWNGDgUf6CXpiKmuQKlsM5RGMy26r2caic7NaGGbvfQC5rdVAMAONkW/CUysAI9h6KnsF6rCxMBWzzEnDr52lkPDnK7n0ZWZ24ElCs2Ra+PmvKINv6MFXY7/ZimoCupm2G8bGmuzVhyGSIe1cJt4z8Og5kBWF8rej9891yMSt+f2exFrFvI9wNBFCtJPYmHDz6MZY3LUBYp817p5Fag6yww1icQJHqVCyjDm3wM5jOr02aibSaYkoRWsT2PlhEDiaJvxmINIXYvC+MwfHn1lIwDno39G/vqeeqmQ9GTXKH+qNFbOmEo/v2xPaaa4sdASt30asbSW3VhQjWOZzifMkMrY+YsucunhJsFKLPlH64IXbOVC9l2gs2F5vpyvH7kXEGreQBQFtXQGU8CYL3XdTOb1M1emqPHAPzoPfOw71R74DiW3qjRK4tqmDqqCsfOdWU1fiOXgfIEQYRj88nNONN1xr/bJgDse9L42by0f4wisFmfiFvVJ6EhiSQ06J1N0BPViFS9jmTb3HybRwwAij7QE+Rau/bBpeFmcgm/p6fNM4Jq9MLEHj2t0Vs4tg4/es9cLB43JHC9geRsOd+KytwdV3NFBFzO0R5+/OS98zFtVBVimorn7l6J+vLsO2vVlHk3DOotwjRj6SnNQ8oBWKmrV00bjl0nL/TJsXpCWVRFRzyFsqhmdN2Um7Fk3XWTp7cLz6WThuKjy8ehqdbnCbwDv6wAxoDymIYZjdXB2/dCoGccL3fFupBrNgmiWNmwfwNKtVIsbQwI4vZvAoZMBKpG9p9hg5zN+iS0ag9hCjuErbwFgIJk20xE6p4FlA5AD/fdTxB+DJjUTdkh/btVkzF5RGWv7l84UHp44caToIHpQU61lfbVs+MDwKrpI81ZZn4MJF/Le7xCb9XoGftOhFT0rpg63ExbHFld2qfKXK70pOFIWFrSgZ4oUfzxnfPx+GeX99HRcqcspqE9noLQm2y3Uo6KXjax8/CqEvzdqsmh0xl7Gpf3lsJqPhjLoga1jxvQEsSgJaEn8MihR7C8aTlKNZ9ZtKkEcPBZStvsZzbrEwFAqtNLp2+yFLTKbfkyixhAFJ6XmSVeztNHl4/Dhr/t3WLi0vSA6J4/6Xb+7a7bCqK/lLYBJOh5qKi9V6MXVY37IqyiVwz0R+rm2HSgV+iUR1V0dCelZizWsmxPjdSLsneM88BP8Q+rPqq99LnIpXkV9wqmCYLoMS8efxHnus9hVXNAt82jrwCJdmrE0s+cQD2O8CGYLwV6elcj9HgdIlVb82gZMVAo+tTN3upGmYm/u3oyasuiWDOzZykNwhH73FWT0n/Ly8Js36PDh6a3xjgUAs7gOKL2oqKXbpLRPZACPenU9NV90FwkgV5ZVENHPAUO9xy97LtuGj/z8dEKe0yh6PVUXcupeZU4P30YCBPEYGTDgQ2oiFRgScMS/5X2bwLAgOaAdYg+4SV9Ei5RtsH4EjSKBBJtMxGt3wSmtoOniuP/S6IwKUpFb+Jwa9CnbjpPfescVJVE8NmrJkHrhQDhwLdW42MrxgOwP9sP03Wzv5yggVyjp6ms11LURGfXRHLg5J31h6KXKXW4UDBq9JLg3GOOXtbnJvuB6b1F2COqvdSkqCfNqwbQVw9B5J1EKoFHDz2KlaNXIqoG1HfvfxIYMQMoq+s/4wgARp3eMHYWo9lb5mvJtplgTIdW+UYeLSMGAkUZ6D30KSuHXBT7F2tgku1crv56mwOpRs/5EEBTlF5T9IZWGs1UasqKI3AJQ7YjPwYyZTEV7TZFzyLbM2M+lOol2/z4aet83LVkrO21bBW9npJL86qB86iEIAqHZ489i/Px88HdNhOdwOEXKW0zT5h1etKYBb17JPTuITQ8negxRRnoyYhi/2INTGS7gxou9HejgoGcPqUpvZe6ee3MUfjOzTPxkUvH9cr+CgE5MA5qxd9TLp8yrOCVvfKohnhSRzLFjYQam9qZbepm/9SgrZxsNfyxCFmjJ1I3e2iDeGiVTfMq8/z08NgEQVhsOLABVdEqLB652H+lwy8CqW5qxJIndvFGtPEyzJPq9ACGxPmZUMv2gann82YbUfwUfY2eeEperC257TU/eTTEwUAWcjRV6bVmLIrCcMv8pl7ZV6EgK8t9+bn6r/ct6LN99xZl6SZM7fEk4Jyjl+W+zKHr/fDZyvUQvafoZT9eIZ81jAQxEOlOdePxw4/jyjFXIqIGPFTb/ySgaMCYgGCQ6DM4FGzWJ2KhLdAz0jdjQx6DVvUGEu/QtSFyo/gVvWLvyS2nyRVQpBe2nXsxoikMkV6qRRqIDOBLnzVlUeNZWHt3Egz2c5O9oteLhmXA3d033HamotdL80Jz+X4eyNkEAsbYKsbYTsbYHsbYFzyWf5oxtp0x9jpj7FHG2Jh82EkUN08ffRrtifbgbpuA0YilYR4Q692xVER4ntenYLxyDCNx2nxN7x6BVPcwaJWUvknkTl68XcbYdxhjO9L/id3HGKvJdV/WfLnidA5sA9ML6D0UjiW9j6b23niFgUgh3Yf5pjyWVvS6U+nPas8b1fRHIOM8Qtgj9tbnIrfxCoMDxpgK4IcArgYwFcDtjLGpjtVeBTCfcz4TwL0Avt2/VhIDgY37N6I2VouFIxf6r9TVZoxWoPq8vPKoPhcAsFJ91fZ6sm0m1LIDYFpbPswiBgD5kjUeBjA9/Z/YLgB357ojzou9Ri9cM5b+doIGsq8f6cXUzYHIQL722WIqevEkgF6ao9cfqZuOixhWfRRdN3v6fXP97AYAwMKx2XfwGwT330IAezjn+zjncQD3ALheXoFz/jjnvCP95/MAGvvZRqLI6Ux24okjT+DyMZdDUwKqdA49B/AUBXp5Zi8fhYP6MKxU3IEeY5xUPSJn8hLocc4f4pwn03/26D8x8cS4WFMNZavDvIVB4AT1OSqlbgZCip5FScS4TzrjqfR0I4tsT1N/NhtxpW6G3K63avQWj6vHgW+txrihFZlXTlPsWfhZ0ADgsPT3kfRrftwF4C9eCxhjH2KMbWaMbT516lQvmkgUO5uObEJnsjO42yZgpG2qMaAxQPUj+gGGx/Q5uER5AyXoNl/V48OQ6hqBCHXfJHKkELzdD8DnPzEg839kutnJrjidU9nsMDV6g8gZ6jM0hRXtg4H+gAI9CzEnMZ7SwRizd93MMWQr5POr9tLA9FzgKO7v8r6AMfYeAPMBfMdrOef8Pznn8znn84cOHdq/xhEFzcYDG1FfUo/5w+cHr7j/SWD0RUCkpH8MI3x5VJ+LEpbAxco22+vJtllQyw7h+IXjebKMKGb6LNBjjD3CGHvD49/10jpfApAE8D9++8n0H5mp6BWpb8BsNT9F+iaKjN4arTBQKdbPUl8Q0dKpjBw9VvT0fhqvAHjU6PXzHL1cGEQPsY4CkFv1NqZfs8EYuxzAlwBcxznvdi4nCD/aE+3YdGQTrhhzBVQlYEROxxngxFZK2ywQXtQn4wIvwWWO9M1E20wARvBOENnSZx4v5/xyzvl0j39/BADGWCuANQDezXvQ4m3OaKOPy8ThxdktKlunr79iwYHc+a6QupsWIvTAwSIqPRRgzK7G5dp1s1+asbhq9MJt11tdN4lAXgIwgTE2ljEWBfAuAH+SV2CMzQHwYxhB3lt5sJEoYh4//Di6U91YNTZDt80DTxk/aX5eQRBHBE/pM9INWazvYJ6oR6qzEQ/ufzB/xhFFS766bq4C8HkY/4l1ZFo/iFvmNeKpz6/Agubsi/4LgbAOWH87XnwA98CLpBuxMAa0XtycX2MKEIqDLaKaFOjBMUcv6xq93LbLhRkN1ba/wwaXWgHUrg702y9dn/5xABsBvAngt5zzbYyxrzLGrkuv9h0AFQB+xxjbwhj7k8/uCMLF+n3rMap8FOYMmxO84r4ngWgFMCrDekS/8Zg+ByPZGUxlB22vJ87Nxptn3sS+s/vyZBlRrOTrf/UfAKgE8HD6P7Ef5bojxhia6sp6z7J+ZiArZ4WKcGb3f3M1vnLdtDxbU3gUcg1ZfyMres6ParZnaXhVDIA1hL0vmdVUg9e/cqX1QraKXh/YFJbBcPtxzh/knE/knI/jnH8j/do/cM7/lP79cs75cM757PS/64L3SBAGb3e+jeeOPYfVLauhsAwu3v5NwJhLgKBh6kS/8njKCLrd3TdnQWEK1u9bnw+ziCImX103x3POm6T/xD6SDzsKgUJ1agrhyX5fQaMVgqFAzyJiU/TQI0Xv6zfOwHdvmYU5o2t7x7gMVJVk77zlM625P7uSEsRAZcP+DdC5jtUtq4NXbDsGnN5N9XkFxtuoxhZ9nGueHk9VYvHIxXhw/4PQuZ4n64hiZOB680VCtk5NfymAQytj+OZNM/rlWP3NQA5iewM6PRbOGj1b86QsP4sVMQ1r5+VnHFpYS/Ma6OXtyAQxcFi/bz2m1E3BuJpxwSvuF/V5FOgVGo+m5mA224t6nLO9vrplNY5eOIotb23Jk2VEMUIuXZ4Jq57cMt9o0jayuv9aIN++cHS/Has/IUUvGNHII0rdSe2BHpgtCC4m4TNs45h8dt0UUDMggsiNfef2YdvpbVjTsibzyvs3AaW1wPDpfW8YkRWP6XOgMI7lymu21y8bfRlKtVJK3ySygjy5PBPWp/nAJc3Y98/XoLY82rcGDQIKwZktZMQYgMoSLc+W5B+5GQtgV/GSevFoUEWh6BXP6SSIguSBfQ9AYQquHnt18IqcG/PzmpdSCkcBso034wSvxUr1FdvrZZEyrBy9EhsPbEQ8Fc+TdUSxQZ/wPBP26TVjNOS7t9BIqQqkMqbhk5dNwG8+vCjfpuSdiKT+MmZ/MNPWmciDRbkReo6eeL95HZje/8cmiGKHc44H9j2ARSMXYWiZe+awjXcOAOcOU9pmwcLwWGo2lilbEUHStmRNyxq0xdvw1NGn8mQbUWyQx0sMOiIUMAfCGMOnrpiI8cOKczZlb6KpijluwqjRszhXRIFeWAphYDp9Ogkie7ac2oKjF46GTNt80vhJ8/MKlsf0uahknVig7LC9vmjkItSV1OGBfQ/kyTKi2KBAjxh00MB0IhtE+qZzjl5RKXohwyc1ncaV1yxKkvQIImvW712PUq0Ul42+LPPKOx4EqkcDQyb0vWFETjyjT0M3j+Ayx5gFTdFwzdhr8MThJ9AWb8uTdUQxQYEeMeig1E0iGyLp+8WIP6wg5GwxBXphUzep6yZBFB2JVAIbDmzAiqYVKItkmCvceRbY+xgw7Xp6qFLAdKIEz+pTcZnyCpzfjmta1iChJ/DwgYfzYxxRVJDHSww6ItR1k8iCmNSQpSuRMn8vptTNbJux8Dx0RqHUTYLIjaeOPoW2eFu4tM2dfwH0BDD1xr43jOgRj+pz0aycRAs7bnt9av1UNFc1U/dNIhQU6BGB/Kx1Af7yyaX5NqNXodRNIhssRY+hO2kNqi2m1M2w0VN+O9JSMxaCyIX1+9ajrqQOi0ctzrzy9vuB6iagYW7fG0b0iMdTswEAKx3pm4wxXDvuWmw+uRnHLxz32pQgTCjQIwJZMXkYpoysyrcZvUqEUjeJLLBq9IC5o2vw92umAjAGoA80TEUvjzZkO4ieIAYzbfE2PHn4SVwz9hpoSobvpM6zwJ5HgamUtlkMHMVQvKk3uer0AOCasdcAAB7YT01ZiGCK1lN57u6VaO9OZl6RIBzQHD0iG+QaPcYY7loyFkMqopjRUJ1ny8ITNnjS8jhTi+boEUT2PHzgYcT1eJZpmzf0vWFEr/CYPgcfUh9AFdptrzdWNmLusLn4894/467pd4Ue1UUMPopW2hhZXUrt34mcyKczSxQfUQ8F+PrZDWgZWpEHa3IjrA8gPhr5CLqWTxoGAJjZWDwBNEHkm/X71qO5qhlT66dmXnn7/UBVI9A4v+8NI3qFR1NzEWEpLFW2upatblmNfef2YceZHR5bEoQBebzEoEOlZixEFkSk1M1iJaztSh6fCq+aPgI7vrYK04tIKSWIfHL8wnFsPrkZa1rWZFZ0us4Z3TYpbbOo2MLH4wyvwEr1Fdeyq5qvgqZo1JSFCIQCPWLQodJ/ckQWxKRmLMVKWNtFPeJtC6g7FvgAACAASURBVJr60hxfSiJqXo5LEMWIqM+6puWazCvv/AuQigPTKG2zmNCh4HF9NlYoWwA9ZVtWHavGsoZleHD/g0g5lhGEgAI9YtBBXTeJbIhoxv0yGO6aiKrgza+uwj+sCZEGRhBE3uCcY/3e9ZgzbA6aKkM8mNl2P1DVADRQ2max8VhqLurYBeDIZteyNePW4O3Ot/HCiRfyYBlRDFCgRww6qBkLkQ1R28D04iQb20ujKhT6jBBEQbPznZ3Ye25vuCYsXeeAvelum1SjXnQ8pc9AgqvArr+4li1rXIbKSCUe2EfdNwlv6BNPDDrIiSWyQTObsRTvfVO8lhME4cX6veuhKRquHHNl5pV3bjDSNqnbZlHShnI8r08B3vg9oOu2ZTE1hiubr8QjBx9BR6IjTxYShQwFesSgozxKdUBEeMRzgcGi6BEEUdgk9SQe3P8gljYsRU1JTeYNtt8PVI4CGhf0vXFEn/Db1HLg7CFg32OuZatbVqMj2YHHDz/e/4YRBQ8FesSg4sC3VksKDUFkRnSiLO5YqbitJwjC4pmjz+BU5ylcN+66zCt3tVlD0ilts2jZqC8AyuqBzT9zLZs3fB4aKhpw3+778mAZUejQp54gCCIARooeQRAFxL2770V9ST0ubbo088q7NgCpbuq2WeTEEQFmv9vonnr+hG2ZwhTcNOEmvHDiBRxqO5QnC4lChQI9giCIAIp5rAJBEAOLk+0nsenIJtw44UZElEjmDbaJtM2FfW8c0bfMawV4Cnj1l65FN4y/ASpT8fvdv+9/u4iChgI9giCIAKzUzeIN+IrXcoIgZO7bcx90ruOm8TdlXrmrDdjzCDD1OkrbHAjUjwPGLgNe/oVrpt6wsmFY1rgM9++5H4lUIk8GEoUIffIJgiACGBjNWIrYeIIgAAApPYX7dt+HRSMXoakqxOy8XRuNtE3qtjlwmPd+4NwhYK+7KcvNE2/Gma4zeOLIE/1vF1GwUKBHEAQRgKnoFXGsVMSmEwSR5rnjz+FY+zHcPPHmcBtsvx+oHAk0XdS3hhH9x+Q1QNkQ4OV1rkWXjLoEI8pH4N5d9/a/XUTBQoEeQRBEAMz8WbzhUjEHqQRBGNy7617UldRhZdPKzCt3nwd2PwxMobTNAYUWBeakm7K0HbctUhUVN42/Cc8eexZHzh/Jk4FEoUGffoIgiADMtEcKlgiCyBOnOk7hycNP4vpx1yOihmjCItI2qdvmwGPu+3ybstw44UYoTMEfdv8hD4YRhQgFegRBEAEoAyDOK2Y1kiAI4I97/4gkT+KmCSGasADAtvuAihFA06K+NYzof+rHAS3LgVfcTVlGlI/A0oalRlMWnZqyEBToEQRBBGLV6BVvsFTEphPEoEfnOu7ddS8WjFiA5urmzBt0X6BumwOdee8Hzh0G9jzqWrR2wlqc6jyFTUc25cEwotCgbwCCIIgAhJ9EsRJBEPngheMv4OiFo7h5QsgmLNv+ACS7gGk39q1hRP6YdA1QPhR4+WeuRUsbl2JY6TD8fhfN1CMo0CMIggiEDYSum0VsO0EMdu7ddS+qY9W4bMxlmVfmHHjuh8DwGcDoxX1vHJEftCgw5z3Arg1A2zH7IkXDDRNuwNNHn8bxC8d9dkAMFijQIwiCCIBiJIIg8sXpztN47PBjuG7cdYipscwb7HkUOLUDuPjj9IRnoDP3fQDXgVfcTVlELecf9lBTlsEOBXoEQRABmDV6ebajJ1AzFoIoTv60909I6snwaZvP/bsxO29ayKYtRPFSNxYYt9KzKUtDRQMubrgY9+2+D0k9mScDiUKAAr0C4Ket8/HAJ5bk24wBzczG6nybQBQpZtfNIn46XsSmE8SghXOOe3fdi7nD5qKlpiXzBie2AvueABZ+yEjtIwY+81qBtiNG8x0HN0+4GSc7TuKZo8/0v11EwUCBXgGwcvJwTBtFgUhfct9fX4I937g632YQRQgbCIpeMRtPEIOUl068hEPnD+HmiWHVvP8AImWG808MDiZdA1QMBza7m7Jc2nQp6kvqce+ue/NgGFEoUKBHDApUhUFT6XYnskcp4oHplulFaDxBDHLu3X0vKqOVuGLMFZlXbjsObP2d0aCjrK7vjSMKAzViXPPdG4FzR2yLIkoEN4y/AZuObsLJ9pN5MpDIN+T5EgRBBFDMwVLxWUwQBAC80/UOHjn4CK5tuRYlWknmDV78T0BPAos+2vfGEYXF3PcZ3VZf+m/XorUT1kLnOu7bc18eDCMKAQr0CIIgArBq9PJrRy4MhNEQBDEY+dPePyGhJ7B24trMK8fbgc0/BSavBupC1PIRA4vaMcDU64CX/gvoPGtb1FTVhEUjF+EPu/+AlKNhCzE4oECPIAgigGLuuskcPwmCKHxSegq/2/U7zBw6ExNrJ2beYMuvga6zwMV/0/fGEYXJ0s8C3W3ASz9xLbp54s043n4cm45syoNhRL6hQI8gCCKAYlbFWBGrkQQxWHniyBM42HYQd065M/PKegp4/j+AhvlA00V9bxxRmIycCUy4ymjI033Btmjl6JUYUT4CP9/+8zwZR+QTCvQIgiACUIo4SGKk6RFE0bHujXVoqGjA5WMuz7zyzr8AZ/YBiz9GT3QGO8s+C3SeAV5eZ3s5okRw55Q78fLJl7H11Nb82EbkDQr0CIIgAijmZizFaDJBDGa2vLUFW05twZ1T74SmaJk3eO4HQPVoYMp1fW8cUdg0LQTGLgOe/Tcg0WVbtHbiWlRGKvGzbe4xDMTAhgI9giCIAJRiTt0UP4vQdoIYjKzbtg5V0SrcOP7GzCsfeRk49JzRaVMNERQSA59lnwMunAS2/Mr2cnmkHLdMugWPHnoUh9sO58k4Ih9QoEcQBBHAgKjRy68ZBEGE4MC5A3js0GO4bdJtKIuUZd7guR8AsSpjjhpBAEDzUqBxIfD094FUwrbo3VPeDYUp+MX2X+TJOCIf5DXQY4x9hjHGGWND8mkHQRCEHwOhRo8VY5RKEIOMX27/JTRFwx1T7si88tlDwPY/AvPeB5RU9b1xRHHAmKHqnTsEvP5b26JhZcOweuxq3L/nfpztOuuzA2KgkbdAjzHWBOBKAIfyZQNBEEQmROom53k2JAdI0SOI4uB052n8ce8fcd246zCkNMSz7xd+bPy86CN9axhRfEy4AhgxE3jqu0ZXVonWaa3oSnXhnp335Mk4or/Jp6L3rwA+D6AI3SeCIAYLQtErxkBPISWPIIqCe3beg+5UN9479b2ZV24/Dbz8c2DajUB1Y98bRxQXjBkdOM/sBbbfb1s0vnY8ljQswf/u+F90Jbt8dkAMJPIS6DHGrgdwlHP+Woh1P8QY28wY23zq1Kl+sI4gCMKimNMeqRkLQRQ+nclO3LPjHixvXI6WmpbMGzz5LSDRYTjzBOHF5GuBIZOATd8FdN226P3T3o8zXWfw531/zpNxRH/SZ4EeY+wRxtgbHv+uB/BFAP8QZj//n737jpOivv84/vrs7t3B0aso3S6CFVETNdhb1BhjRQNGRY2oscRoNLaERGOKiahojBViookFf4pdNBoLRQQVFUSqCEev13Y/vz9mDvaOu+XAu53b5f18POaxu/Odmf3s3M3OfPb7ne/X3e939/7u3r9Tp06NFa6ISK2qkiTPxcYHuTw0hMhW4tkZz7K8bDlD+g7Z9MIln8P4v8O+Q6Dzbo0dmuSqWAwOvgoWfQJfvFitaL8u+9GnQx8e/eRRUp6qYwOSLxot0XP3I9y9b80JmAn0Bj4ys1lAN2CSmXVprFhERLZUVfPHVO7mearRE2mikqkkj376KP069mOfzvtseoWXfwWFLeDQXzZ+cJLb+p4C7XrBW3dUu/fAzBiy+xBmrZzFG3PfiC4+yYqsN91096nu3tnde7l7L2AesI+7f5PtWERENiWX79HL5WanIluD1+e+ztxVcxmy+5BNH69fvg7TXwqabLZQZ+WyCfEEHHQFfD0JZlZP6I7seSRdW3blkU8eiSg4yRaNsCkiksH6XjdzsOlmVZKqfK9pMbNjgL8AceABd7+tRvkhwJ3AHsAZ7v7v7EcpW6LXtc9vxtJOca97sHh7ho4sA+peN0aK5wuvowWdOOK5npQ/tznvI1utPc+EcbfDW3+AHQ5bPzsRS3BOn3O47YPbmLxoMnt13ivCIKUxRT5geliztzjqOERE8o1q9JoeM4sDdwPHAn2AM82sT43F5gBDgH9kNzrJpnjz2cSbz6V86cFs6nLstPg4dovN5XeVZ1FOQXYClNyXKILvXg6z34FZb1crOnnHk2ld2Fq1enku8kRPRKQpi23ojSVnKeFrUgYAM9x9pruXA/8ETkpfwN1nufsUQD0l5LGCDm+SqiymYvm+GZdrwTquSjzB+NTOjE0NyFJ0kjf2HQwtu8Abv612D0JxQTGn73I6r815jdkrZ0cYoDQmJXoiIhnEcjjPsxqP0iR0BeamvZ4XzttsGn4od8UKF1HQahoVyw4EL8y47MWJMXSylfy64hx0NMtmK2ge9MA5+x346s1qRWftdhaJWIJHP3k0ouCksSnRExHJIBZmep6DvbGY7tHLaxp+KHcVdhiHpxJBopdBV0q4IP4CTyUPYorvkKXoJO/s82No3XWjWr2OzTtywg4n8OyXz7JwzcIIA5TGokRPRCQDW98ZS+6pil3j6DUp84Huaa+7hfNkKxErWkCizYdULPsOnmyZcdlrCv6FA3dUnJ6d4CQ/FTQLavXmvg8zXqtWdEG/C0h5ins+uiei4KQxKdETEckgl1OkXI49j40HdjKz3mZWCJwBjIk4Jsmios4vQqqIssUDMy63t03npPj/uD95PAvokJ3gJH/tfQ606QFvDK9Wq9etVTdO3+V0npnxDF8u/zLCAKUxKNETEclg/fAKOVilt6EfmRwMPk+5eyUwDHgJmAY84e6fmNmtZnYigJntZ2bzgFOB+8zsk+giloYUL/6SRMvPKV98KKSKMyzp3FAwikXelpGVJ2YtPsljiUL43s+DcfW+eLFa0dA9hlKcKObOSXdGFJw0FiV6IiIZVHXGksrBTK+qyWYOhp7X3P0Fd9/Z3Xdw9+HhvBvdfUz4fLy7d3P3Fu7ewd13jzZiaRhOUeexpCraUL7sOxmX/H7sPfaNTeeOytNYS7MsxSd5b88zoV3vjWr12jVrx0/6/oRxc8cxaeGkCAOUhqZET0Qkg1hO36MXPOZi7CL5JtFqKvHm8ygrORK87rHwiinluoJ/8GmqJ/9JHpLFCCXvxQvge7+Ab6bCtOeqFZ3d52w6Ne/Enyb+KSc7H5PaKdETEclgfY+VOXje29DsNAeDF8krSYo6v0SydBsqV+yTccmrEk+yLUu5oeJcUrpMk4bW71TosBOM+x2kNgzV2TzRnJ/u9VM+KvmI1+e8HmGA0pD0DSIiksGGGr3cTZaU54lEq6DdB8QKl1BWciyZLr362UyGxF9kVPIIJvnO2QtQth7xBAy8FhZ9Cp8+Xa3oBzv+gN5tenPnpDupTFVGFKA0JCV6IiIZ5PIYdOubbirRE4lOrIzCjq9RuaY3ydW71LlYnCS3FfyNxbThjkoNpyCNaPeTodNuMO42SCXXz07EEvxsn58xa+Usnp7xdIYNSK5QoicikoF63RSRb6Ow/VvEEqspW3QsmQY9+Ul8LLvHZnNjxRBWkalHTpFvKRYPavUWfwFT/12t6NDuh7JXp724Z/I9rK1YG1GA0lCU6ImIZJDLtWIaKF0kWhZfRWGH/1Kxsi+p0h51LtfNFnFF4j+8ktyXl1L7ZTFC2WrtdiJs0w/evA2SG5ppmhlX9b+KxesWM2raqAgDlIagRE9EJINcvkcvl5NUkXxQ2PE1sErKSo7OsJTzm8RDpDBurBhCplo/kQYTi8Gh18HSmTDln9WK9uq8F4d1P4wHP36QpaVLIwpQGkIi6gBERJqyXG66+afT9uLOV7+ga7vmUYcistWxgsUUtPuAiuX74eWd6lzuhNi7DIx/xM0VP2YBHbIYoeSSXtc+3whbdcYU9qbd0zdzxL9aUEbh+pJY4V4Ub/8GB977S8oWntBg7zjrtuMbbFuyaarRExHJIJfHotu3ZzseO29/CuL6qhfJtqLOL4EnKC85os5l2rCaGwseZXJqex5NHpXF6EQAjNsrz6B7rITz4mOrlaTKO1OxfD8K2r2HFSyJKD75tnT2FxHJIKbmjyKymWLNZ1PQeirlSw7Gk63qXO7axOO0YzXXVVygMfMkEu+k+vFicj+GJZ6hC9UTuvLFR4DHKOr8YkTRybelbxURkQwsl0dMF5HsswqabftvUhVtKF96SJ2LDbBpnJl4gweSxzPNe2YxQJHqflM5iBgprit4vNp8r2xN+ZKBFLSeSqLlJxFFJ9+GEj0RkQxy+R49Ecm+wo6vEi8qoXTBKZAqqn0ZKvhdwQPMSXXizsofZjlCkermeWfuS57ASfH/sZ99Vq2sfPFAkqXbUrTt0xBfE1GEsqWU6ImIZKD6PBGpr1izORR2eIvyZQNIrtm5zuUuSzzFDrEF3FD5E0qpPRkUyaZ7K09gvnfgloJHiJFKK4lT+vWpWHwtzbZ5LrL4ZMso0RMRycDU07mI1ENZsoxm2z2JV7ambNFxdS53YOwTfhofwxOV3+Ot1J5ZjFCkbqUUMbxiEH1iszkr/lq1slTZdpQvPoyCNpPVhDPHKNETEakHV9tNEcngnsn3pDXZbFbrMu1ZyZ8L7uEr78JNlYOzHKFIZi+k9ud/yT5clXiStqyqVla++NANTThjayOKUDaXEj0RkQxyeXgFEcmOKSVTePiThylftl+GJpvOHQX30Y7VXFpxKeuoPRkUiY5xS+WPacVarko8WaMsrQlnlzGRRCebT4meiEgGhjpjEZG6lSXL+NU7v6JT806ULap7MOifxF/k8PiHDK88i0+9V/YCFNkMn3sPHkseyVnx1+hjs6qVBU04D1UTzhyiRE9EJAPV6IlIJvdMvoeZK2Zyy3duqbPJZl+bybWJf/Bycl8NjC5N3p8rT2E5Lbmp4FFqnv02NOF8Rk04c4ASPRGRDGz98ApK9USkuqklU3n4k4f54U4/5Ltdv1vrMi1Yx10Fd7GYNlxTMZQNffmKNE0rackdlaezf+wzToy9W6M0ETbhXEOzLuqFs6lToicikoEuyUSkNmXJMm545wY6Ne/E1f2vrnO5Wwseooct4mfll7CcVlmMUGTLPZEcyJRUb64r+AfFlFYr29CE80PiLT+NKEKpDyV6IiL1oAo9EUl37+R7mbliJjd/52ZaFdaewP0w9hanxN/mr5U/5APfLcsRimy5FDFurhjMtraUyxJPbVQeNOHsQjP1wtmkKdETEclgwz16yvREJPD2/Ld56JOHOHnHkzmo60G1LtPbFvDrgod4P7UrdyVPznKEIt/eJN+ZxysP5aLE//H9DE04m2/3BFQbZF2aCiV6IiIZqNdNEUk3fdl0rn7zanZsuyO/GPCLWpcppIK7Cu6inAIuL7+ElC63JEfdVDmED1K78MeCkexjX1QrS5V1pWzhCSRafUZR5xciilAy0TePiEgG62v0lOiJbPWWrFvCpa9fSvNEc+4+/G5aFLTYaJkiyhlZ8Gf6xmZxTcVQvqFDBJGKNIxyChhafiVfe3v+VvhHutvCauUVyw6kfOmBFHZ4m4K270cUpdRFiZ6ISAbqjEVEIOh85fI3LmfxusXcddhddGnRZaNliinloYLfMzD2EddWnM8rqf4RRCrSsJbTinMrriGG83DB72nN6mrlZQu/T+XqnSnq8izx4ukRRSm1UaInIlIPukdPZOvl7vzqnV/xUclH/Pag39K3Y9+NFypdwaOFtzEg9hlXVlzMP5OHZT9QkUYyy7flwvIr6G6LGFlwJwVUppXGWTf/LFJlnWjebTSxwkWRxSnVKdETEclgwzh6EQciIpEZOWUkY78ay2V7X8ZRvWoZ8HztUnjkRPawLxlWcRnPpGrvoEUkl33gu3FNxYV8J/4pv008QLXB1FPNWDdvMO4Jmnd/BIuviSxO2UCJnohIBrpHT2TrNvarsdwz+R5O3OFEzu93/sYLrFoIDx8Pi6YxtOJKXkwNyH6QIlnyTOog7qz8Iacm3uKS+LPVyryiPevmnYMlVtCs22NglXVsRbJFiZ6ISAZV9+ip6abI1mfyosnc8PYN7NN5H2468Kb1NfzrrZgHDx8Hy2bDoCcZl9o7mkBFsujOylN4KnkQPy94ghNi/6tWllrXk9IFPyJRPItmXZ4CnTsjpURPRCSDjS7sRGSrMH/1fC5/43K2abENdx56J4XxwuoLLP0KHjwWVi+Cc56G7b8XTaAiWWdcW3EB76d25Q8F93FYbFK10sqVe1FWcgQFbSdR2GFcNCEKoERPRKRe1HRTZOuxYPUCLnrlIiqSFYw4fATtmrXbUJishA9HwYNHQ/kqGDwGeuwfXbAiESingAvLr+Ar78KDhX/g7oI76cKSDeWLD6dixZ4UdX6JgrbvRRjp1k2JnohIBuvv0Ys2DBHJkunLpnP22LODYRQOv4vt22wfFKRS8MkzcO+B8Owl0LorDHkBtlNzTdk6LacVJ5b/hjsqTuPw2Ie8VnQ1F8T/jwSVgFG64EdUrtqVZts+Q2HHV9CZNPuU6ImIZLD+Hj1V6YnkvYkLJzL4xcG4Ow8f8zD7brNvUJ0/41X420B4cjBYDE4fBRe8Dtv0iTpkkUiVU8DdyR9wRPkd/C+1O9cX/IPnC3/JAJsGXsC6eedQsXxfijq9RlGXp6lMqYOWbFKiJyKSgWr0RLYOr815jaEvD6VDsw6MOm4Uu7TfBea8Dw9/H0adAuuWwQ9GwsX/g91O2PDlICLM885cUHE155dfRQsr5YmiX/PHgnvoyGpKF/yIssWHUtjuA64cdyWllaVRh7vVSEQdgIhI06Zx9ETy3ROfP8Hw94fTt8PujOg3jHYf/Rumvwyz/gstOsOxd8C+gyFRFHWoIk3aq6l9ebusL5cknuXC+HMcWTSRl5L78eqSfXi78ljG8SJDXxnKXYfdRZuiNlGHm/eU6ImIZKAaPZH85e6MnPAn7vn0YQ5OtOMP0yZSPP7YoLDTbnD4jbD/RVDYItpARXJIKUX8sfI0nk4exKWJpzk6PoFTE29RtjbBG7YPvyz5iMHPn8XIo/9OlxZdog43r0WW6JnZpcAlQBJ43t2viSoWEZG6rG+cpSo9kdznDsvnwPyJJOdNYPj8l3kyUcZJq1Zz05pVFGx/KOx4OGx/KLTpGnW0Ijltpm/HFRWXkKCS/rEvODw2iQuWf077tQu4bJtKznniKEZuexQ79D0DttsHYrqjrKFFkuiZ2aHAScCe7l5mZp2jiENEZFOqxtFTmieSg9Ytg/kTYf6k8HEirCnh60ScGzt15P1mRZzfpi+XHX4Vtt3eEItHHbFI3qkkwXupPryX6sMFlx3PgMXTeXjKKC6e8wznLBjLdR+P5vveEtv5aNjlONh+IBQWRx12XoiqRu9i4DZ3LwNw90URxSEiktGGXjcjDUMkr/W69vkG2U4zyhgQ+4zvxj7m4NjH9InNXl82PdWVyb4rz7baiykdZ5LCKPv6BP48bT/+/N5C4MUGiUFENqHjTux62C2MWj2Ua8ddzS9jH/NyvA03fjaGTh8+BolmQbK38zGw6/HQUvVBWyqqRG9n4GAzGw6UAle7+/iIYhERqdOGe/SU6Yk0NTFS9LWvOCg2lYNiH7Nv7AuKrJIyTzAxtTN/qDiVSb4TU1PbszpRRrNt/0Oi5edUrtmB0gWn4BXto/4IIlutri278vBxoxg1bRR3fXgXP+jRnWt7DeP7yxZjX7wIX7wIL/wcdj8ZDrgIuu4bdcg5p9ESPTN7FajtDsvrw/dtDxwA7Ac8YWbbey0DVZnZUGAoQI8ePRorXBGRWnVqFfSy990dOkYciYgEnD1sJj+Kv8X34+/S3lYD8GmqJw8nj+adVF/Gp3ZhHc3WL1/Q9gNadH4BzCld8AMqlg9AI0yJRC8eizN498Ec0u0QfvXOr/jlF4/xcveB3HjBq3RaXQKTHoMPR8HUJ6DbgCDh2+1EiBdEHXpOsCgGATazF4Hb3f2N8PWXwAHuXpJpvf79+/uECROyEaKIyHpzl65lu7bNicc0bla2mNlEd+8fdRy5ItfPj/VputmZZZwcf5sfxd9ip9h8Sr2Al1P9eTW5L++kdmcJG3fVbonlYS3edNXiiTQBs247vs6yZCq5vnavKF7Edftfx/G9j8fKVsHk0fD+fbDsK2jdFfY7D/Y9F4q3zuO5vufIqJpuPgMcCrxhZjsDhcDiiGIREcmoe3vdFC4ShSLKOSo2gVPi/+Xg2BTi5oxP7cy1FefzfPIAVlHHsRlbS2H7dyls/5Zq8URyRM3avev+ex3/9+X/cdGeF7HXARfDgKHB+Jbv3Quv3Qpv/h72PAMOHAYdd4o6/CYpqkTvQeBBM/sYKAcG19ZsU0RERLY+vWwBg+KvcWr8TdraGuZ7B+5OnsRTyYOZ5dvWuZ7FV1HQ/m0K272HxcuoWLUbZQtPUC2eSA7p3aY3jxzzCKOnjeZvU//GOWPPYUCXAZzf73wO2PkYbJdjYeGn8P5ImPw4THwYdj4WvnMp9PzOhpvrJZpEz93LgbOjeG8RERFpeuIkOTw2ibPjr3JIfCoVHufF1H48njyMd1N98Ay1cZZYRmGHtyhoOx4sSeXKPShfMpBUWd1JoYhk3+b1sNsB7EoK2r3PexX/5YNvhpJc153yxQOpXL0bcDQdOIAfJ17hnM9fpv0XY/kotT1/qzyesakBJGm84VIyNUFtSiIbMF1ERESEVd9wafwpzky8zna2lPnegT9UnMq/kodSQtuMq1phCUUdxpFo8yFgVKzYm/LFA/EKdZ4kkhe8kIqlB1Ox7EAK2kyksMObNO/+GMnSbShfcihLVvbjz5U/4t7KEzgl/l/Oi7/AiMK7mOcdR/sBkAAAIABJREFUeajyGJ5OHsRSWkf9KSITSWcsWyrXbzYXEZH6UWcsmyfnzo+rS+CLsfDZCzDjFUhV8layH48lj+T11N4Zf4m3xHISradQ0Hoq8eZz8VSCiuUDKF9yCF6ZOTEUkVyXJNF6CoUd3yBetIhUZQsqV/WlcmU/kmt7YxhHxCZxQeJ5BsQ+J+nGB6ndGJvaj5eS+7GQhmnGHXWNXlPvjEVERES2JotnwOfPw2fPw9wPAIc23WH/ixg4rnfme+8Sy0m0nkpB6ynEm88FIFm6HWWLjqZi+X54smWWPoSIRCtO5cq9qVy5J/GWn1HQejIFbT6ksN3765O+11f245W1N9DH5nJM/AOOjY3n1oJHuLXgESaldmRscgBjUwOY5/k/ELsSPREREWlYZath6cxg+vpD+PwFWPxFUNZlDxh4LexyHHTpB2bMeqPGfTtWQbzZfOLFs0i0/JR48RwgLblb2U/NM0W2ajGSq/uQXN0HrJxEy89JtJpKQZtJ65O+L1f15a9rduXP645m+4rVHBv7gGPi47m+4B9czz+YlurBh6kdmeLbMzXVmy+8OxV5lhrl16cRERGRxuEOFeugbCWUrgwfV8C6ZbBsFiz9CpZ+GSR3qxduWC+WgJ7fhf0ugF2OhbbdN9q0JVYSbz47nOYQaz4fsyQAydJtldyJSN28kMpV/ahc1a9G0hfU9AF8U9GWB9b25L51x9NxXSuOq5jFobGpHB9/j7PsdQDKPME078HHqd5M8e35JNWLmb4t62gW5af7VpToiYiIbK0qy2DFPFi1ANaUwJrFsHpR+Dx8vaYkSObKVkKqsu5ttewCHXaAnY6E9ttD+x3Cx+2hqCXuztLSpcxeOInZK2fz1cqvmL1iNp8t/YyWO30NgKcSJEu7Ub7kIFLrepBc11PNMkWk/tKTPiqJNVsQ/IBUPId48SwK2nzEauBfqQL+UdqVVNlutKloxvblZexZuYzvVs7nxPj/ONteW7/Jed6R6amuzPBwSm0Haw/MicHa1RmLiIg0OfncGYuZHQP8BYgDD7j7bTXKi4BHgX2BJcDp7j4r0zbrPD+WrYKVC2DlfFg+Z+Np1QKg5nWAQXEHaNEJWnaC4o7QvB00aw1FrYPHZm3TnrfB23RnjUHJuhIWr1vMknVL1j//Zs03zFk5h9krZ7OqYtX6dymIFdCjVQ92bLcjz75XQHJdT1Kl26LfoEWksVhiOfHmc4gXzybWbB6xwsXEEmvWl7vH8Ip2FJW3omNFjG2T5fRMrmHn1DL6JkvomiqjXTIVfEsVd4B2vaF97xqP20PLzo06np86YxEREWlizCwO3A0cCcwDxpvZGHf/NG2x84Bl7r6jmZ0B3A6cnmm7X8+fyz03nMM2tpQuLGMbC6ZWtm79Mg6UeYx5dGA2HZnnvZnHvsynPYusJUutOcusiFVWiJcnsYoKWFGBxSogVobFl2GxBVhsHcRLsVgpVvWYWB0sV4On4nhla1LlHUmV9w0fO5Eq74hXtGUpMSZ/670qIlI/XtmWylVtqVy1x4aZsbVBwle4mFhR8FhaWMLXzZexIF7KpPULdgo3Au0TzWnrRsvUClotfZ9WC9+gZTJJq5TTMpWipSVoXtSKZoWtKWrWlmbN2lLUvD3NijtSVNyRohYdKUgUk0g0oyDRPJgKmpNINMcShWCxoMVFZRkky6CyHCpLNzyvJyV6IiIi2TMAmOHuMwHM7J/ASUB6oncScHP4/N/ACDMzz9AEZ03RSv7TeyIVxKg0I0kBSbYhaZAiSPI8lkpfI5zmbrSt5nW8h7tBqghPNsNT4VTRhlSqC17ZklRlKzxtSlW2glRzoPF+1RYR+dZSxaRKe5Aq7bFxmVVgiVVYfDWxxKrgeWIV3yRWsTC+dsMPXolS4kVrIVaa9l3rwAqoXAGrZ8Pq+oUTdyfhToyg2UfMPXyEOL5Zw8DnVKI3ceLExWY2O+o4NqEjsDjqIOohV+KE3Ik1V+KE3Ik1V+KE3Ik1V+LsGXUAjaQr1bOrecD+dS3j7pVmtgLoQI2/m5kNBYaGL1e/fv60zxsl4saTK/+LmeT6Z8j1+CH3P0Ouxw+5/xlyMf56nSNzKtFz905Rx7ApZjYhF+4ryZU4IXdizZU4IXdizZU4IXdizZU4ZdPc/X7g/qjj2FL58L+Y658h1+OH3P8MuR4/5P5nyPX4M4lFHYCIiMhWZD6QPr5At3BercuYWQJoQ9Api4iISL0p0RMREcme8cBOZtbbzAqBM4AxNZYZAwwOn/8IeD3T/XkiIiK1yammmzkiV5rR5EqckDux5kqckDux5kqckDux5kqceSm8524Y8BLBffYPuvsnZnYrMMHdxwB/Bx4zsxnAUoJkMB/lw/9irn+GXI8fcv8z5Hr8kPufIdfjr1NOjaMnIiIiIiIim6ammyIiIiIiInlGiZ6IiIiIiEieyftEz8y6m9kbZvapmX1iZpeH89ub2StmNj18bBfO39XM3jWzMjO7usa2rgi38bGZPW5mzep4z8Hhdqeb2eBwXiszm5w2LTazO2uJc5qZrTKzheF73ZkW53/N7E0zm2JmH5jZxIaOM5x/pplNDd/nRTPrWMc+nR2+f4mZXZu2TxeZ2VozczPr2Fj7dFOx1vjbzw736Qwzu6Xm397M/mpma6KIs577dF34fzHVzL42s1ebwD5dFsb0sVU/nt4Ip+lm9k74vxrlPl1mZpVmtiCcXxXrkrR9+rKZHdyE9+krZnaNmX0Wvp4fxT5NW+5BC47zj2vM3zPch1PN7Dkza13b+iJVzOzX4f/b5PA43C7qmDaHmd0RHpdTzOxpM2sbdUyby8xODb83UmaWM13Mm9kxZva5Bef1a6OOZ3PV9T2aK6yO6+tcYmbNwmuUj8LPcEvUMTU4d8/rCdgW2Cd83gr4AugD/B64Npx/LXB7+LwzsB8wHLg6bTtdga+A5uHrJ4Ahtbxfe2Bm+NgufN6uluUmAofUjBMoBo4L49yTYNDcB8NlpgDPh89/CLzQ0HESdNCzCOgYLvd74OZa9ml/4EugXxjrZwQdCFwL7A3cDqwgGISyUfbppmJN26fxcJ2vwn26CPhT2t/+EeAxYE0UcdZnn6b/nwL/AS6Jcp+Gz48GZgPTqX48vQmMC5//Oty/Ue3TfYBDgIOAMtKOfaB12j69DHi4Ce/T+4FZQBHB8XRUFPs0bRuHhPv24xrzxwPfC5//BPh1Y323a8qPCWid9vwyYGTUMW1m/EcBifD57YTXErk0AbsBuwDjgP5Rx1PPmOME58vtgULgI6BP1HFt5meo9Xs0VybquL6OOq7N/AwGtAyfFwDvAwdEHVdDTnlfo+fuC9x9Uvh8FTCN4ILoJIILUMLHH4TLLHL38UBFLZtLAM0tGNeoGPi6lmWOBl5x96Xuvgx4BTgmfQEz25ngYu2/NeN097Xu/kIYZ2egJfBpuFgRsGv4/GmCi9eGjtPCqYWZGcHFcLX13X0BwQExw92nhrG+A5wIPOLuHwJ3hu/dmPs0Y6xpf/sBBBfOUwj2qQGl4WKPAacC1wSrZD/OqljJsE/DxR4hSPAPAx6LeJ/i7i8Bn4fvl348dSUYGwzgHuCAbMeZHqu7vwXMI0j01h/77r6SDcd+C2BdE96nXYC4u5eFx9PL2Y4zXbhPl9ZStDPwVvj8FeCU2tYXqRIeh1VaADnVQ5y7v+zuleHL99jw3Zcz3H2au38edRybaQDB+XKmu5cD/yT4zswZGb5Hc0KG6+uc4YHV4cuCcMqp76BNyftEL52Z9SKobXof2Ca8uAb4Btgm07ruPh/4AzAHWACsCC+2aupKUAtXZR4b/+OfAfzLw58QMsT5GcHF1lNh0USCX1AATib4BaV5Q8bp7hXAxcBUgou8PgS1SrWunxbrewS/zKbv03htny+iWJex4W9fTLBfIRijytLijjrOTe3T7YDXalwcRRFr1f/p7sA6qh9PHcOpKuaNjq1sxhnqBjRj42N/GLATMAi4sbYVm8g+7QF0MrP3LWi+vV+UcWbwCRsutk6l+sDgIrUys+FmNpcMx2GO+AkwNuogthL1udaSLKlxfZ1TzCxuZpMJWrS84u459xky2WoSPTNrSdDk7Wc1L5LDhCtjBm/BPXwnAb0JLrZbmNnZWxjOGcDjm4jzSoLmWuXuPjMsvhpImNmHwPeA+TXj/rZxmlkBwcXe3uH6U4Dr6lg8Ecb6MzbUkAHZ2aebEWsz4Ahq/O3De0FOrRl7hHHCpvdpIXX872Qz1rT/01uBVC2b8rSYN/o/yOY+DWO9F/imlmP/eoImxqMJkr7a1m8K+7RqzNMDgJ8TNMuMJM5N+AnwUzObSPBDVPlmri95yIJ7ij+uZToJguPQ3buT4TiM0qbiD5e5Hqgk+AxNTn0+g8iWyHR9nQvcPenuexH8IDzAzPpGHVND2ioSvfAC5j/AaHevqh1baGbbhuXbEmTymRwBfOXuJeEv308B3zGz/W1DBysnEiRf6b9idwvnVcWyJ0F7/onh63ja+r+pipPgPr3pwJyqOAkumL9y972B68N5NZOUbxvnXgDu/mV4kf5EuH73tPUvIqip+X7aPu0GrKyxT2tLALId608JLhwWp/3t1wIrCS5odwZamtksoNiCAYqjiLM++3R3gmP2+SawT6v+T18K119/PAGLgSVhzHUdW1nZp2nH/jMEf/NqsabFN5q6mxk2hX1aAiwKm5l8QHBsFUexT+vYR4TrfubuR7n7vgQ/SHyZaXnZOrj7Ee7et5bp2RqLZjoOI7Op+M1sCMF396C6WupEbTP+Brki47WWZEcd19c5yd2XA29Q43arXJfY9CK5zcyMoPnRNHf/U1rRGGAwcFv4uKkvuznAAWZWTNCs6nBgQljFu1fa+7UHfhv+ug7Bjdrpv4qfSVqNjLsngb3COB8haOPcHmgDnE9wc3dVnBenxXkd8GAjxNkM6GNmndy9BDiSYN/NrVo/jPVRgrbMT5tZIUEt5XNU36drM+3Qxo41bZ++D5xgZr3ZUAvazN2fN7M/Ae3d/RozW+3uO5rZzU10n/4mXC9TDWS29uk0d/+TBc01oPrxlH6yrevYytY+rTqm/k7Q3Hl9rGb2H4KLymcJasI+a8L7dDlhMyUL7vEtZOPjq9H36aaYWWd3X2RmMeAGYGR91pOtl5nt5O7Tw5eZjsMmycyOIbjH+3vuvqlznjSc8cBOaef1M4Czog1p65Lh+jpnmFknoMLdl5tZc4Lz3u0Rh9WwvAn0CNOYE0GHJU7QDGlyOB0HdABeI6g1e5XgYh+CTg/mEfz6vzx83josu4XgJPQxQSceRXW850+AGeF0bo2ymcCuGeKcFj6WEvwaXtUxR1VnIl8S9Gw0urHiBC4K33MKQaLRoY5YvwrjLCO4UK3ap4vC+ZUE9/pEEmuNv/2XYUwLCBKm2v72a5rwPp1OcJ/hjxr7/3Qz9ukygg5Bqv7On4ZxvhlO0wk65pgf8T5dHsbpBLWNl4X7dHU4fRKuv1cT3qevEdSwfRwuVxLFPk1b7nGCY6kifP/zwvmXE3w/fUGQoFrU5wBNTXsiqA34OO1/rmvUMW1m/DMIfoSpur7IqV5Dw89wMhs6rFoIvBR1TPWMu6qH8i+B66OOZwvir/V7NFcm6ri+jjquzfwMewAfhp/hY+DGqGNq6MnCDyoiIiIiIiJ5Yqu4R09ERERERGRrokRPREREREQkzyjRExERERERyTNK9ERERERERPKMEj0REREREZE8o0RPREREREQkzyjRExERERERyTNK9ERERERERPKMEj0REREREZE8o0RPREREREQkzyjRExERERERyTNK9ERERERERPKMEj0REREREZE8o0RPREREREQkzyjRExERERERyTNK9ERERERERPKMEj0REREREZE8o0RPREREREQkzyjRExERERERyTNK9ERERERERPKMEj0REREREZE8o0RPREREREQkzyjRExERERERyTNK9ERERERERPKMEj0REREREZE8o0RPREREREQkzyjRExERERERyTNK9EREREQkJ5jZLDM7Iuo4RHKBEj2RHGVmN5vZqKjjEBGRrYuZnWVmE8xstZktMLOxZnZQ1HGJSHVK9ETqEP5quMjMWqTNO9/MxkUYVr2ZWVszu9fMvjGztWY21cwG12O9PmY2xsxWmNkqM3vdzA7IRswiItK0mdmVwJ3Ab4FtgB7APcBJm7mdRH3mZYuZxaN6b5HGokRPJLM4cPm33YgFsna8mVkh8CrQEzgQaAP8HPi9mV2WYb0dgHeAqUBvYDvgGeAVMxvQ2HGLiEjTZWZtgFuBS9z9KXdf4+4V7v6cu//czIrM7E4z+zqc7jSzonDdgWY2z8x+YWbfAA/VNi9c9vtmNtnMlpvZ/8xsjzriqfP9wvJrwhrHr8Mfat3MdgzLHg5/DH3BzNYAh5rZ8Wb2oZmtNLO5ZnZz2rZ6heufG5YtM7OLzGw/M5sSxjqisfa9yJZQoieS2R3A1WbWtrZCM/uOmY0Pa7/Gm9l30srGmdlwM3sHWAtsH877TXjiWm1mz5lZBzMbHZ5YxptZr7Rt/CU8oaw0s4lmdnA94z6H4FfWU939q/BE/CJwGfAbM2tZx3o3A++6+/XuvtTdV7n7X4FRwO31fG8REclPBwLNgKfrKL8eOADYC9gTGADckFbeBWhP8CPk0NrmmdnewIPAhUAH4D5gTHoCV5/3M7NjgCuBI4AdgYG1rH8WMBxoBbwNrAF+DLQFjgcuNrMf1Fhnf2An4HSCms3rw/fYHTjNzL5Xx74RyToleiKZTQDGAVfXLDCz9sDzwF8JTkZ/Ap43sw5pi51DcDJrBcwO550Rzu8K7AC8S/ArZntgGnBT2vrjCU5g7YF/AE+aWbN6xH0kMNbd19SY/x+gmOBkXdd6T9Yy/wng4Hq+t4iI5KcOwGJ3r6yjfBBwq7svcvcS4BaC812VFHCTu5e5+7o65g0F7nP399096e6PAGUECd3mvN9pwEPu/om7ryX4IbOmZ939HXdPuXupu49z96nh6ynA40DNxO3X4bIvEySGj4fvPx/4L7B3HftGJOuU6Ils2o3ApWbWqcb844Hp7v6Yu1e6++PAZ8AJacs8HJ5kKt29Ipz3kLt/6e4rgLHAl+7+anjifJK0k4S7j3L3JeH6fwSKgF3qEXNHYEHNmeF7LAZqfpaM64Xz4gQJp4iIbJ2WAB0z3Eu3HRt+1CR8vl3a6xJ3L62xTs15PYGrwqaQy81sOdC9xnbq837bAXPTytKf1zrPzPY3szfMrMTMVgAXEZwX0y1Me76ultd1tZgRyToleiKb4O4fA/8HXFujqOYJhvB117TXtZ1Y6n2SMLOrzWxa2DR0OcG9djVPOrVZDGxbc2Z4cu4ILDazQWHz0dVmNjbTeuE8JzjJi4jI1uldgtq1ms0Zq3xNkKhV6RHOq+K1rFNz3lxguLu3TZuKwx9TN+f9FgDd0sq61+O9/wGMAbq7extgJGC1rCeSE5ToidTPTcAFVE/iap5gIDjJzE97XdtJrV7C+/GuIWh+0s7d2wIrqN9J51XgWEvrMTR0ClAOvO/uo929ZTgdm7beqbVs7zTgPXcv25LPIiIiuS9siXIjcLeZ/cDMis2swMyONbPfEzR1vMHMOplZx3DZzR0G6G/ARWHtmplZi7CTlFa1LJvp/Z4AzjWz3cysGPhVPd67FbDU3UvDDsjO2szYRZoUJXoi9eDuM4B/EXRmUuUFYGcLxhNKmNnpQB+C2r+G0AqoBEqAhJndCLSu57qPAfMI7unrFZ6Ijya4n/CO8GRdm1uA74SdyLQ3s1ZmdilwLsEJVEREtmLhbQRXEnR6UkJQAzeMoIfm3xDc2z6FoPfmSeG8zdn+BIIfVkcAy4AZwJA6Fq/z/dx9LME5741wG++F62T6wfKnwK1mtorgnPfE5sQu0tSY+xZXOIjkNTObBZzv7q+Gr7sD0wlqtgaG8w4C/kLQo9cM4HJ3fzssGweMcvcH0rZZbZ6Z/Qbo5u5DwtdHACPdfUcLxvT5G/Ajghu+/0xwEjrf3V8Nu33e0d3PriP+9sDvCJrYdCC4x+6XwO3unsrwufsCtwGHAC2AVcCZ4UlTREQk55jZbsDHQFGGzmRE8ooSPZGtgJkVEHT8Mh8Y4vU88M2sG8GvoDe5+98bMUQREZEGZWYnE7S+KQYeAVLuXtf9hSJ5R003RbYCYY+fpwBfUr9eO6vWmwccC2ybYew9EanBzB40s0Vm9nEd5WZmfzWzGeFgy/uklQ02s+nhNDh7UYvknQuBRQTnviRwcbThiGSXavREREQamJkdAqwGHnX3vrWUHwdcChxHMADzX9x9/7DJ9QSgP0FnThOBfd19WdaCFxGRvKAaPRERkQbm7m8BSzMschJBEuju/h7Q1sy2BY4GXnH3pWFy9wpwTONHLCIi+aauAS+bpI4dO3qvXr2iDkNERBrZxIkTF7t7p6jjaERdqT7O5rxwXl3zN2JmQ4GhAC1atNh311133aJAFq0qY+HKmmNYwzatm9G5VdEWbVNERBpPfc+ROZXo9erViwkTJkQdhoiINDIzmx11DE2du98P3A/Qv39/39Lz42vTFnLp4x+ytjy5fl5xYZy7ztybw3fbpkFiFRGRhlPfc6SaboqIiGTffKB72utu4by65jeagbt0Zq/ubbFkOXiK4sI4e3Vvy8BdOjfm24qISCNToiciIpJ9Y4Afh71vHgCscPcFwEvAUWbWzszaAUeF8xpNPGY8dt7+dJr+HG3nvcNdZ+7NY+ftTzxmjfm2IiLSyHKq6aaIiEguMLPHgYFARzObB9wEFAC4+0iCsb2OA2YAa4Fzw7KlZvZrYHy4qVvdPVOnLg0iHjOKl8+kePlMNdcUEckTSvREREQamLufuYlyBy6po+xB4MHGiEtERLYearopIiIiIiKSZ5ToiYiIiIiI5BkleiIiIiIiInlGiZ6IiIiIiEieUaInIiIiIiKSZ5ToiYhIVo0eDb16QSwWPI4eHXVEIiIi+UfDK4iISNaMHg1Dh8LatcHr2bOD1wCDBkUXl4iISL5RjZ6IiGTN9ddvSPKqrF0bzBcREZGGo0RPRESyZs6czZsvIiIiW0aJnoiIZE2PHps3X0RERLaMEj0REcma4cOhuLj6vOLiYL6IiIg0HCV6IiKSNYMGwf33Q8+eYBY83n+/OmIRERFpaJH3umlmcWACMN/dvx91PCIi0rgGDVJiJyIi0tiaQo3e5cC0qIMQERERERHJF5EmembWDTgeeCDKOERERERERPJJ1DV6dwLXAKmI4xAREREREckbkSV6ZvZ9YJG7T9zEckPNbIKZTSgpKclSdCIiIiIiIrkryhq97wInmtks4J/AYWY2quZC7n6/u/d39/6dOnXKdowiIiIiIiI5J7JEz92vc/du7t4LOAN43d3PjioeERERERGRfBH1PXoiIiIiIiLSwCIfRw/A3ccB4yIOQ0REREREJC+oRk9ERERERCTPKNETERERERHJM0r0RERERERE8owSPRERERERkTyjRE9ERERERCTPKNETERERERHJM0r0RERERERE8owSPRERkUZgZseY2edmNsPMrq2l/M9mNjmcvjCz5WllybSyMdmNXERE8kGTGDBdREQkn5hZHLgbOBKYB4w3szHu/mnVMu5+RdrylwJ7p21inbvvla14RUQk/6hGT0REpOENAGa4+0x3Lwf+CZyUYfkzgcezEpmIiGwVlOiJiIg0vK7A3LTX88J5GzGznkBv4PW02c3MbIKZvWdmP6hjvaHhMhNKSkoaKm4REckTSvRERESidQbwb3dPps3r6e79gbOAO81sh5orufv97t7f3ft36tQpW7GKiEiOUKInIiLS8OYD3dNedwvn1eYMajTbdPf54eNMYBzV798TERHZJCV6IiIiDW88sJOZ9TazQoJkbqPeM81sV6Ad8G7avHZmVhQ+7wh8F/i05roiIiKZqNdNERGRBubulWY2DHgJiAMPuvsnZnYrMMHdq5K+M4B/urunrb4bcJ+ZpQh+kL0tvbdOERGR+lCiJyIiW2T0aLj+epgzB3r0gOHDYdCgqKNqOtz9BeCFGvNurPH65lrW+x/Qr1GDExGRvKdET0RENtvo0TB0KKxdG7yePTt4DUr2REREmgLdoyciIrUaPRp69YJYLHgcPXpD2fXXb0jyqqxdG8wXERGR6KlGT0RENrKpGrs5c2pfr675IiIikl2q0RMRkY1sqsauR4/a16trvoiIiGSXEj0REdnIpmrshg+H4uLqZcXFwfx6+fxzeP75LY5PREREMlOiJyIiG9lUjd2gQXD//dCzJ5gFj/ffv4mOWJJJGDMGjjoKdt0VLr4YUqkGj11ERESU6ImISC3qU2M3aBDMmhXkarNmZUjyliyB3/8edtgBTjoJPv0UfvMbGD8+6OlFREREGpw6YxERkY1UJW3fapy8SZNgxAh4/HEoLYWBA+GPfwySvYROPyIiIo1JZ1oREanVoEFbMCZeeTn8+99Bgvfuu0E14JAhcMkl0LdvY4QpIiIitVCiJyIi3978+XDffcGNegsXwk47wZ13wuDB0LZt1NGJiIhsdZToiYjIlnGHt96Cu++Gp54KbtY7/ngYNgyOPFL334mIiERIiZ6IiGyeNWuCEdVHjICpU6FdO7jiiqAXze23jzo6ERERQb1uiohs9UaPhl69ggq4Xr2C17WaMQOuvBK6doULL4R4HB54AObNgzvuUJInIiLShCjRExHJYzWTuJ/+dOPXQ4fC7NlBS8zZs4PX65O9VApeeAGOOy647+6uu+DYY+Htt4NeNc87b+NxGERERCRyaropIpKnRo8Okra1a4PXs2fDvfduKJ89G0aODBK8dGvXwu3XLmPQwoeC++9mzoRtt4VbboELLgiei4iISJOmRE9EJE9df/2GJK8uNZO8PfiIS7ibs+eNgqt9ntlAAAAgAElEQVTWwcEHw+9+ByefDAUFjResiIiINCg13RQRySPpTTVnz67fOgkqOI1/8RYH8xF7cTajeKbl2TB5ctCr5mmnKckTERHJMUr0RERySFUiZwaJRPDYsWMwmcE552y4325TurCAG7mF2fTkX5zBdnzNlfyRnZrPx0feD3vu2eifR0RERBqHmm6KiDRxo0cHzTBnzw6SuaokLpkMHpcs2bDsphM85zv8j2GM4Ef8mwIq+bjHsfyy9AEeW3QM3XvG+P1wGDSoMT6JiIiIZIsSPRGRJqxmhyr1qamrTTFruaT941waG0H3xZNZThseaXUpnW+6mBOv2omHgYcbKGYRERGJnhI9EZEmavRoGDx4Q83dlujNTH7R+l4ujP8dli6Dfv1g+H20HTSI81u0aLhgRUREpElRoici0gRV1eRtSZJnpDiSVxjGCI7neVgdg1N+CMOGBb1omjV8wCIiItKkKNETEWmC6jM0Qk2tWcEQHuYS7mZnplMS68wnJ95AvxEXQteujROoiIiINElK9EREmqA5c+ouq+qQJR4Pavy+2+Zjziu9m1PLHqMlayjZ6UC4+WY6nXIKnYqKshe0iIiINBkaXkFEpAnq0aP2+fE4PPYYeEUllf/6Dz7wUN5e0Y9zeYiW554GEybQ6Yv/wVlngZI8ERGRrZZq9EREmpjRo2H16o3nFxfDI3cs4kez/ga9R8K8edCzJ9x+O/zkJ8FgeiIiIiIo0RMRidTo0XD55dXHwtuYc2TrD7iv3wh6X/EElJfDkUfC3XfD8ccH1XwiIiIiaSJL9MysO/AosA3gwP3u/peo4hERybbRo+Hcc6GiovbyIko5nX8xjBHst3ICTGkFF14IP/0p7LprdoMVERGRnBLlPXqVwFXu3gc4ALjEzPpEGI+ISNZUjZFXW5LXg9n8luuYRzceYQgtWMMl3A3z58Nf/6okL0eY2TFm9rmZzTCza2spH2JmJWY2OZzOTysbbGbTw2lwdiMXEZF8EFmNnrsvABaEz1eZ2TSgK/BpVDGJiDS2uptqOofxOsMYwYmMAeBZTmIEw3iDQ+nZ07i7VdbDlS1kZnHgbuBIYB4w3szGuHvNc9y/3H1YjXXbAzcB/QlavEwM112WhdBFRCRPNIl79MysF7A38H60kYiINJ6qQdDTx8drySp+zKMMYwS78RkldOR2fsFILmIuQdebxcUwfHhEQcuWGgDMcPeZAGb2T+Ak6vdj5tHAK+6+NFz3FeAY4PFGilVERPJQ5MMrmFlL4D/Az9x9ZS3lQ81sgplNKCkpyX6AIiIN5PLLNyR5u/AZf+VS5tOVuxnGKlrxYx6hO3O5nt+uT/I6dID774dBgyIMXLZEV2Bu2ut54byaTjGzKWb27/De9Xqvq/OjiIhkEmmiZ2YFBEneaHd/qrZl3P1+d+/v7v07deqU3QBFRBrI6NGwbEmSE3mWlzmSz9iNodzP05zMAN5nfz7gMX5MRawZEIyaMGoULF6sJC+PPQf0cvc9gFeARzZnZZ0fRUQkkyh73TTg78A0d/9TVHGIiDS6xYuZMfTvzOQeejKHOXTnOn7L3zmPEjoDQfNM1dzllflA97TX3cJ567l7+p2aDwC/T1t3YI11xzV4hCIikteirNH7LnAOcFhaj2PHRRiPiEjDmjABhgwhuV03blp7LTPYkZN5iu2ZyW1ctz7JU/PMvDQe2MnMeptZIXAGhL3shMxs27SXJwLTwucvAUeZWTszawccFc4TERGptyh73XwbsKjeX0SkUZSVwZNPwogR8P77VBS14G+V53E3P+VTdt9o8Q4dguaZkl/cvdLMhhEkaHHgQXf/xMxuBSa4+xjgMjM7kWC4oaXAkHDdpWb2a4JkEeDWqo5ZRERE6qtJ9LopIpLz5s2DkSODqrmSEmbEduYv/JVHy37MStrUudpf/pLFGCWr3P0F4IUa825Me34dcF0d6z4IPNioAYqISF5ToicisqXc4c03mXPNCLYb/wwxUjzHCYxgGK+lDsc30Tq+Qwc11xQREZHGoURPRGRzrV4No0axfPgI2s77hBa0549cxUguYha967WJ4mLV5omIiEjjUaInIlJfX3wB99wDDz0EK1cyk725iwf5J2dQSvN6byYeV+crIiIi0riU6ImIZJJMwtixfP3LEWw39SXKKeBJTmUEw3iPA9jcPqXM4JFHlOSJiIhI41KiJyJSiyfvW8rHVz7I4LX3sD1f4WzHr7iVv3EBC+myxdu96CIleSIiItL4lOiJiKR54beT+eZXIzgzNZpTKeVNDuEX3M4z/IBKCrZ4ux06BPfkKckTERGRbFCiJyJSXg5PPcWMK0Zw3DfvsIZiHuXH3M0lTGWPLdqkEjsRERGJkhI9Edl6ff01U4bdzzbP3Mc2/g2wA1fwJx5mCMtpt9mba9ny/9u78zC5qjLx49+3E0IIAcIuSxaEiASEgBFcRgUSFRwkLiho0LBogKQRxxXN/BgVmcF9RjsEwiJbyyIuLILILqMDshiWgEgEQhKWhGDYAglJ3t8ft1o6oZfqdFdXd9X38zz91L3nnnvve2+nKvX2OfecYio9kztJklRtJnqS6ksm/PGPPPaVJra77Zfsxiqu4UCaaORaPtDp3HftOe64YkBOSZKkvsBET1J9WLYMfv5zaGqCe+5hE4bxEz7PTI7j7+y0zoe1i6YkSeqLTPQk1ba//50HPj+Tba4+m01Zyj3sThOz+DmfYhkbdvlwDQ1wzDG23kmSpL7NRE9S7Vm9mqaDf8+o3zbxQa5mNAP4FR+liUb+l3+hK3PfRRRTIpjYSZKk/sRET1LtWLoUzj2XBd+YQePLc3mKrTmZ/8cspvAE23X5cOPHw/XXVyBOSZKkCjPRk9T/3XcfzJgBF1wAy5bxGO/iK3ybX/IxXmVQlw9n90xJktTfmehJ6p9efRUuv7wYXOWWW2DwYObuM4mP3zKN2ey5Tod05ExJklQrTPQk9S9PPQVnnglnnAELF8KoUdz9ye/z4cuPZP4tm6/TIceMgTlzejhOSZKkKjLRk9T3ZcJttxWtd7/4RdGa94EPwOmn8/N/HMjhkweQ2fXD2kVTkiTVKhM9SX3Xyy/DxRcXCd7dd8PGG/PX/ady2B+mcs+1b4Jru35I572TJEn1wERPUt/z2GMwcyacdRY8+yzsuit/PuI0Dmw+nGev3WidDnnhhSZ3kiSpfpjoSeobMou5DJqa4MoroaGBP2z2YU6ikVvmvBfmlD/33dqOO84kT5Ik1RcTPUnV9fzzcN55xfQIDz0EW27JNXt+gyl3H8OCxcO7ffjx430GT5Ik1R8TPUnV8cADRXJ3/vnw4ov8uWEffsIF/GLxx1mxeP0eOYXTJUiSpHploiep96xcWXTLbGqCG2/k1QHr07zqMGYwjTtXv63HTjN+fNELVJIkqV6Z6EmqvMWLi4FVZs6E+fN5duhwvsd/cfaqo3mGLXvsNBFw7LG24kmSJJnoSaqcO+6ApiZWXHAxg3IF1zOeJn7CVS8exKoe+PgZOBDOPdeBViRJktZmoiepZy1fDpdeWnTP/POfeYGhnMfnOI2pPMiYHjmFLXeSJEkd6zTRi4gdgQWZuTwi9gV2B87PzKWVDk5SPzJ/Ppx+Oq80ncng5xfzV3amiZ9yPp/hBTbu9uF97k6SJKl8DWXU+SWwKiJ2AmYBw4GfVzQqSf1DJtx0E7du9TFWjRjFqv88ld89/04mcB278CAzaOx2kjd0aDHZuUmeJElS+cpJ9FZn5krgI8BPM/MrwDaVDUtSn/biizBzJo9ttBvsvz+7LL6F7/MVduTvfITfcAMTgO5NcJ5Z/Lzwgs/gqX+KiAMi4qGImBsRJ7ax/YsR8UBE3BsRN0TEyFbbVkXE7NLPFb0buSSpFpTzjN6rEfFJYDLwoVLZepULSVJfdcX3H+Kxr53G5DyXTXieJezFN/kZl3Aor7BBt449eHAxMKdJnWpBRAwAZgDvAxYAd0TEFZn5QKtqfwHGZeayiDgO+B5waGnby5k5tleDliTVlHISvSOBY4FTMvPRiNgBuKCyYUnqK6Ydu4rHz7iaRpo4mN+zgvW4lE/QRCO3sw/dabkDEzzVrL2BuZn5CEBEXAxMBP6Z6GXmTa3q3wYc3qsRSpJqWqeJXmY+EBFfA0aU1h8FvlvpwCRV2ZIl/GDMOXx50WnswGMsYDv+nZM5k8+xiK3X+bBjxsCcOT0Yp9Q3bQfMb7W+ANing/pHA9e0Wh8cEXcCK4FTM/M3a+8QEVOAKQAjRozodsCSpNrS6TN6EfEhYDbwu9L6WJ8XkGrXIW+8m7PjaF7eYnu+vOirzGMkh/ALduBRTuHf1znJGzy4GFTFJE9aU0QcDowDvt+qeGRmjgM+Bfx3aQTsNWTmrMwcl5njttxyy16KVpLUX5TTdfObFF1QbgbIzNkR8cYKxiSpl40ds4IxD17GNGZwGX/iJYZwHpOZwTTu5y3dOvZxxznfnerSQopRqltsXypbQ0RMAKYD783M5S3lmbmw9PpIRNwM7An8vZIBS5JqS1mDsWTmcxFrPIezukLxSOoFzc0weTJsvWohx3AGv2MWb+BpHmYnvsCPOZcjeI5h63Rsu2ZKANwBjC49174QOIyide6fImJP4AzggMxc1Kp8U2BZaf7aLYB3UQzUIklS2cpJ9OZExKeAARExGvg88KfKhiWpJ02dCjNntqwl7+ZWfk4TH+VXNLCaq/kgTTTye95PljXryus5qIr0msxcGRGNwLXAAOCczJwTEd8G7szMKyi6ag4FflH6Y+rjmXkwsAtwRkSspnjE4tS1RuuUJKlT5SR6x1N0K1kOXETxn9bJlQxKUvdNmAA33PDa+hBeYhLNNNLE7tzHPxjGf/MFZnIcj/C6x3/KYrdMqX2ZeTVw9VplJ7VantDOfn+CbvaZliTVvXJG3VxGkehNr3w4krpj113hgbX+7r8jc5nKaRzFOQzjOWazB0dzFhfxSV5mSJfPYddMSZKkvq/dRC8irgSyve2l7iWSqmjtVrsWwWo+wLU00sSBXMMqBnAZh9BEI3/inazr3He24EmSJPUPHbXo/aDXopBUtvaSO4Bh/IMj+RlTOY2d+DtP8ga+xX8wiyk8xTZdPtf48XD99d0MWJIkSb2u3UQvM2/pzUAkta2jxK7FW7iXRpo4nAsZwsvcyr8wnVP4NR/hVQZ16Xx2zZQkSer/Ouq6eWlmfiIi7qONLpyZuXtFI5PqWDnJ3UBe5SP8mkaaeA+3sowNaGYSM5jGPYzt0vnskilJklRbOuq6eULp9aDeCESqZ20NotKerXmKKcziGM5gO57gEXbgS/yAn3Ek/2Czso5hYidJklTb2p0wKzOfLC1Ozcx5rX+AqT1x8og4ICIeioi5EXFiTxxT6i+am2HgQIgoJ8lL3sGfaOZTPM4Ivs1/cC+7869cxWge5kd8qcMkb+BAuPBCyCx+TPIkSZJqWznz6L0P+NpaZQe2UdYlETEAmFE6/gLgjoi4wklhVQ+22w6eeKLzeoN5mU9yEY00sRd/YSmbMINpnMZU5jK6w3233RYWLuyhgCVJktSvdPSM3nEULXdvjIh7W23aCPhjD5x7b2BuZj5SOt/FwETARE81bdNNYenSjuuM4lGOYyZHczab8yz3sRvHcDrNTOIlhra5z8CBcO65MGlSz8csSZKk/qWjFr2fA9cA/wW07lb5QmY+2wPn3g6Y32p9AbDP2pUiYgowBWDEiBE9cFqp902dCjNndlwnWM0ErqeRJg7iKlbTwK/5CE008gfeQ1tz35ncSZIkqS0dTa/wHPAc8MlSN8utS/WHRsTQzHy8NwLMzFnALIBx48a1O4G71BeVk+BtzHNM5jymMYOd+RtPsxWnMJ0zOIaFbL9GXRM7SZIklaPTZ/QiohH4JvA0sLpUnEB3p1dYCAxvtb59qUzq15qbYfJkWLWq43pjmMM0ZvAZzmcoL/F/vJ1JXMhlHMIK1v9nvQED4LzzTO4kSZJUvnIGY/kCsHNmLunhc98BjI6IHSgSvMOAT/XwOaReU84UCQNYycFcQSNN7M9NvML6XMQnmcE07mIc4ITlkiRJ6r5yEr35FF04e1Rmriy1Fl4LDADOyUy/3qrfKLflDmBLFvFZzuI4ZjKcBcxjBF/jVM7maJawBWCCJ0mSpJ5TTqL3CHBzRPwWWN5SmJk/6u7JM/Nq4OruHkfqLRMmwA03lF//bfyZRpo4lEtYnxVcxwQaaeIqDmI1A/5ZzwnMJUmS1JPKSfQeL/0MKv1IdaUrLXcA6/MKn+BSGmlib+7gBYYyiymcxlT+yi6vq2+SJ0mSpJ7WaaKXmd/qjUCkvqCrSV1rw3mcYzmdz3EmW/IMD/JmptHEBXyaF9h4jbomd5LUt61andz80CLmPPE8u267MfvuvBUDGl4/zY0k9VXljLq5JfBVYFdgcEt5Zu5fwbikXlXONAhtS/bjJhppYiKXA3AFB9NEIzeyP2vPfWeCJ0l936rVyafPvp3Z85fy8opVbDBoAGOHD+OCo/cx2ZPUb5TTdbMZuAQ4CDgWmAwsrmRQUm/o6vN2rQ3lBT7NBTTSxBgeZDFb8F2+xukcy3xGrFF3/Hi4/voeCFiS1CtufmgRs+cvZdmKonvHshWrmD1/KTc/tIjxu2xd5egkqTzlJHqbZ+bZEXFCZt4C3BIRd1Q6MKmSttsOnnii6/vtzF+ZymkcwblszAvcwTg+w3lcyidY/lqDtyNoSlI/NueJ53l5xZp9+F9esYoHnnjeRG8tmbnWeqvlduqtWd66frZZ3v7x26nUi1atTm59eDEPPvkCu2yzEe8eveXrWn3bu5a2dPWK1r7/fU3fjq5t63xLy9hvvYHBkEHlpF89o5wzvVp6fTIi/hV4AtisciFJPa87z941sIqDuIpGmngf17OcQVzCoTTRyB3s/c96ttxJUs947JmXWPLS8s4rVsj6AxtYf2ADr6xcvUbZegMbuPOxZ9vcp6PveB19cezoi3pf/pLcx/OLXrF6dfKf1zzI3EUvsmLlagYNbGCnrYbyjQN3ocEuvmrDFkMHMXrrjXrtfOUket+JiE2ALwE/BTYG/q2iUUk9pDvdMzfnGY7mbI5jJqOYx3y25xucwll8lsVsxcCBcOG5MGlSj4YsSXVv5epkxcrqZRK7bbsJO241lDmPPwMDBrL+egPZcauhvGXbTXh1lRmOCrPnL2XuohdZXvqDwPKVq5m76EVmz1/KXiM3rXJ0Unmjbl5VWnwO2K+y4Ujds+6DqrxmL+6ikSY+yUUMZjk3sh9f5EdcwcGsYqADqkhSjWtoCL5x4C4cc8KXWDV0axqPncLY4cNspdEaHlvyEitatfoCrFi5mseWvGSipz6hnFE3f0YbvQcy86iKRCStg+4meINYziFcRiNNvIPbeJENOYejWHLoNP7fxbviELOSVF8aGoJBS+bCkrnsNfJr1Q5HfdCozTdk0MCGf7boAQwa2MCozTesYlTSa8rpunlVq+XBwEcontOTqq67Cd52LOAYzmAKs9iaRfyN0fxbw//wjtMnM/Vzm/RcoJKkTrXMXXfrw8+w5dD1bUVTnzZ2+DB2WquL705bDWXs8GFViWf16mT2/KU8tuQlRm2+oe8fldV185et1yPiIuB/KxaRVIbuPHsHyXv4A4008RF+TQOruYqDmHdQI8dfPoEfNzT0ZKiSpDKsPXedA1uor+tLXXwdGEZtWZdvtKOBrXo6EKkzzc0wdChErFuStyEvMoUzuJfduYV9Gc8N/IgvMmbQ33nhwis4/sr3g0meJFVF67nrkjUHtpD6qpYuvhvM+yN7jdy0aklV64FhfP+oRaffaiPihYh4vuUVuBKws7p6xYQJRWIXAYcfDi+91PVj7MTD/Ih/YwHbcwbHspKBXLDv2Wz20gK+mt/jr8t3cORMSaqytuauaxnYQlLHOhoYRvWr00QvMzfKzI1bvb5p7e6cUk/qbssdFHPffZDfcjUH8jBvopEm7t76g/DHP7Ln6rv59E1HwZAhPRu4JLUSEQdExEMRMTciTmxj+/oRcUlp++0RMarVtq+Xyh+KiA/0ZtzVsuu2G7PBoAFrlDmwhVSeloFhWvP9ow6f0YuIDYBJwJhS0Z3AZZm5otKBqT5179k72JRnOZKfMZXT2JFHWDZsG/i3b7He5z7H/tts03OBSlIHImIAMAN4H7AAuCMirsjMB1pVOxr4R2buFBGHAd8FDo2IMcBhwK7AtsD1EfGmzFyzuavG7LvzVowdPoz/+9uTZEP1B7aQ+pO+NjCM+oZ2W/Qi4i3AA8C7gcdKPx8A/hgRwyLiO70RoOpDSxfNdU3y9mA2s/gcC9ieH/JlNtp5O7jkEoYsmgcnnQQmeZJ6197A3Mx8pPTH0YuBiWvVmQicV1q+DBgfEVEqvzgzl2fmo8Dc0vFq2oCG4IKj92HLh69kw3n/y+f3H+1AElKZWgaGGfrAb9jg0Vt9/wjouEXvJ8CUzLyudWFETADuB+ZUMjDVvuZmOOaYdXvuDmAgr/JRfkUjTbyb/2UZG7Bwv8MZ/eNpbLXHHj0brCR1zXbA/FbrC4B92quTmSsj4jlg81L5bWvtu11HJ3tk8Uscesb/dSvgJ8ccCtDt43TXc9u+jdWr4ar7nuCq+6o7m9PzY4sHuL99lV951L6+9O/kleHFx0xfeP/o9dYb0MAG6w3ovGIP6SjR22btJA8gM6+PiFcp5tOTuqy7c9+9gSeZwiyO4Qy25Ume2OCN8J0fMuTIIxm96aY9F6gk9WERMQWYAjB0mx27fbyxe4zt9jF6wtg9xvLyq6t4ddXqzitX2Ogxu1U7BAAyk7/NfQQGDGLbbbdh6PoDKBp/e9/DD9wPVP/e9KV7Uu170VpfiKUv/W6g7/ybBXjw/vtoaOi9z9uOEr2GiFg/M5e3LoyIwcCrmbmssqGp1jQ3w+TJsGqdnjJJ3smfaKSJQ7iM9VjJ/SMOZNuZZ7HtAQc4LYKkvmYhMLzV+valsrbqLIiIgcAmwJIy9yUzZwGzAMaNG5eXHPOOHgu+2uYuepHFLyzvvGIdaJkfbfXgYTBgIItfWM4mG1RvfrRpP/86ACd99YpeP3eLvnZP9Jq++LvpC/9mW3zhkulsMGgAl5x2XLeOc+mx5dXr6Nvx+cAvI2JkS0FpRLBLgQvWPTTVo+Zm+PSnu57kbcAyjuJs7mYv/si/8MlNrmG9fzseHn6Y3eZdDR/8oEmepL7oDmB0ROwQEYMoBldZ+1vGFcDk0vIhwI2ZmaXyw0qjcu5AMX/tn3spbvUxLfOjMXAQRIPzo+E96cv83fQt7X5DzszvAL8Dbo2IZyLiGeAW4LrMPLm3AlT/NnXqa3PgZZa/3w48wvf4CgvYnrP5LHu+ZRWccQYsXAg/+hHstFPlgpakbsrMlUAjcC3wIHBpZs6JiG9HxMGlamcDm0fEXOCLwImlfedQ/FH1AYr/h6fV+oibap/zo72e96Tv8nfTt3Q4vUJmNgFNEbFRaf2FXolK/V5zMxx1FKzowkQcwWrex3U00sS/8lsyGhjw8Y/BtGnw7ncXGaMk9ROZeTVw9VplJ7VafgX4eDv7ngKcUtEA1S+0zI+2vNWX53qfH8170nf5u+lbyurzlpkvmOSpI83NsMUWRS7W0oJXbpK3CUv5PP/DX3kz13IA79ngDhpO+n8MmD8PLrkE3vMekzxJUl1qmR9t/YENBLD+wIa6nx/Ne9J39bXfzerVyYrNd+Llke/i7nn/YPXqLnQvqwEdtuhJ5WhuhiOPhFdf7dp+u3Ef05jB4VzIUF5i8eh3wre+xSYf+xgMGlSZYCVJ6kda5kebPX8pjy15iVGbb8jY4cPqetAR70nf1Zd+Ny0Dw7w45sMwYCA/ufFhdtqqvgbtMdFTtx17bPlJ3kBeZSKX00gT+3ILyxsGs/4Rn4Jp09hyr70qG6gkSf1QQ0Ow18hN2WukUwi18J70XX3ld7PGwDCwxsAw1Y6tt3TadTMihkTE/4uIM0vroyPioMqHpr6uZaCVF1/svO5WPM10vsOj7MBlfJyRzGPWTt9j/UUL4OyzwSRPkiRJPcSBYcpr0fsZcBfQMkHPQuAXwFWVCkp934QJcMMNndVK9uF2GmniE1zKIF7lWt7PVGYy/JgPMuP0Ab0RqiRJkuqMA8OUl+jtmJmHRsQnATJzWVRzentVVXMzHHMMvNTBH0MG8zKHcgmNNDGOu3iejZjJcfzjsKl886Kd+UDvhStJ6qd64ptGV6b1kVRbWgaGmbvoRVasXM2gPjAwzPLNdmT5Jttww4NPs+/OWzGgws8KlpPorYiIDYAEiIgdgeUVjUp90tSpMHNm+9tHMI/jmMlnOYstWMIcxnAcp/GbDQ/nB2dsxAmTei9WSVL/tdNWQ9lpq6HVDqND2UEW2VGC2Vnu2eFxO9m3N2w0eD0Axo3adI3rzFbRtXcJ5dRfe9fW9yPXKC834jL10s3NdThRV6+1L/w76SvOPXJv/jj3Gf729Au8aeuNeNdOWzCgITp8n3VkXe/tylXJ8RfdzXO7TCQbBnL8RX9h7PBhXHD0PhVN9spJ9P6DYsLW4RHRDLwLOKJiEalPaW6GE06AJUvaq5GM5wYaaeJDXAnAb/gwMxsaOfK8fZl5eNBBbihJUr/UUeem7rVG9u1OUy3Xtt6Asmbokqruw3tuV+0QuOHBp5nzxPPkgGJgmGUrVjF7/lJufmgR43fZumLn7TTRy8zrIuJu4O0Unz4nZOYzFYtIfUJnXTQ34nk+w/lMYwa78FcWswWnciJncAyLB4/grLNgki14kiRJqnNznniel1esWqPs5RWreOCJ56uT6EXE2sMgPll6HRERIzLz7opFparovPUO3syDTGMGkzmPjXiR29mbT3M+v+DjxODBJniSJElSK7tuuzEbDBrAslbJ3gaDBjBm240ret6OWvR+2MG2BPbv4VhURR09f0PMYA0AAB4pSURBVDeAlRzEVTTSxARuYDmDuJjDmME07mBvAI47Dk47rRcDliRJvWrV6mTZsDeyYsOte20wCakW7LvzVowdPozZ85fy8opVbDBoAGOHD2Pfnbeq6HnbTfQyc7+Knll9RntJ3uY8w2c5i+OYyUge53GG83X+k7P4LM+w5T/rmeRJklTbVq1OPn327Swe/aFeHUxCqgUDGoILjt6Hmx9axANPPM+YbTfuG6NuRsRgYCrwLxQtebcCp2fmKxWNTBXVUTfNt3InjTRxGBczmOXcwP58gf/mSj7Eqlb/ZAYPxq6akiTVgZsfWsTs+Ut7fTAJqVYMaAjG77J1r75fyhl183zgBeCnpfVPARcAH69UUKqstlrwBrGcj/MLGmni7dzOi2zI2RzNDKbxIGPWqNvQUAzUYiueJEn1oVqDSUhad+UkertlZutv+jdFxAOVCkiV0V4L3vbM5xjOYAqz2IrFPMSbOJ6fcD6f4Xk2WaOuXTQlSapP1RpMQtK6KyfRuzsi3p6ZtwFExD7AnZUNSz2l7WkSkvdyC4008WF+QwOruZIP0UQjNzCeZM25cTbfHP7nf+yiKUlSvarWYBKS1l05id5bgT9FxOOl9RHAQxFxH5CZuXvFolO3NDfDkUfCq68W6xvyIodzIY00sRtzWMJm/JAvMZPjmMeoNfa19U6SJLWo1mASktZdOYneARWPQhUxfXqR5I3mb0zlNI7kZ2zC89zFXhzJOVzMYbzCBq/bzyRPkiStrRqDSUhad50mepk5LyI2BYa3ru+E6X3cqlW8Zd41nE4TB3AtK1iPS/kETTRyO/sAr/8LnF00JUmSpNpQzvQKJwNHAH+nmF4BnDC973r2WTjnHF743mlcyaMsZFv+nZM5k8+xiLb/AmcLniRJklRbyum6+Qlgx8xcUelg1A1/+Qs0NcHPfw6vvMJdvJcmvsflTGQl67W5iy14kiRJUm0qJ9G7HxgGLOqpk0bE94EPASsoWgqPzMylPXX8urFiBfzyl0WC96c/wZAhPPzOyXzsxmncx1va3MXkTpIkSap95SR6/wX8JSLuB5a3FGbmwd0473XA1zNzZUR8F/g68LVuHK++PPEEnHFG8fP007DjjvCjH8ERR/C+PTdlXju7RcAzz/RqpJIkSZKqoJxE7zzgu8B9wOqeOGlm/r7V6m3AIT1x3JqWCf/7v0Xr3a9+BatWwQc/CI2N8P73Q0Mx99289rI8YMSIXopVkiRJUlWVk+gty8yfVDCGo4BL2tsYEVOAKQAj6jFTeeml4rm7pia4914YNgxOOKEYQWXHHYFivrwTToAlS9o/TAScckovxSxJkiSpqspJ9G6NiP8CrmDNrpsdTq8QEdcDb2hj0/TMvLxUZzqwEmhu7ziZOQuYBTBu3Lhsr17N+fvfi6EwzzkHli6F3XeHM8+ET30Khgz5Z7W1J0Vvz7HH+lyeJEmSVC/KSfT2LL2+vVVZp9MrZOaEjrZHxBHAQcD4zKyfBK4jq1fDtdcWrXfXXAMDBsDHPlZ0z3zXu4pmuVaam2Hy5KIXZ2ecPkGSJEmqH+VMmL5fT580Ig4Avgq8NzOX9fTx+52lS+FnPyuysblz4Q1vgJNOgilTYNtt29xl6lQ4/fTi0b3OjBzZw/FKkiRJ6tPKadEjIv4V2BUY3FKWmd/uxnmbgPWB66JopbotM4/txvH6p3vvhRkz4MILYdmyotXu5JPhox+FQYPWqNrcDNOnF4OtRJSX4EHRy9Nn8ySp90TEZhTPno8CHgM+kZn/WKvOWGAmsDGwCjglMy8pbTsXeC/wXKn6EZk5uzdilyTVjk4TvYg4HRgC7AecRTFC5p+7c9LM3Kk7+/drr74Kv/lN0T3zD3+AwYOLh+emTYM992xzl+bmonFvWants9wkzznzJKkqTgRuyMxTI+LE0vraUwgtAz6TmQ9HxLbAXRFxbas5Zb+SmZf1YsySpBpTToveOzNz94i4NzO/FRE/BK6pdGA156mnisFUTj+9mAdvhx3g+9+Ho46CzTZrd7euPIfXwgRPkqpqIrBvafk84GbWSvQy82+tlp+IiEXAlsBSJEnqAeUkei+XXpeV/uq4BNimciHVkEy47bai9e4Xvyha8z7wgWKi8wMPLAZb6UBLS165SV4EXHCBCZ4kVdnWmflkafkpYOuOKkfE3sAg4O+tik+JiJOAG4ATM3N5mztLktSOchK9qyJiGPB94G6KETfPrGhU/d3LL8PFFxcJ3t13w8YbF6OnTJ0Kb3pT2YeZPv217pqdiXAKBUnqLR1NIdR6JTMzItrtcB8R2wAXAJMzc3Wp+OsUCeIgiumFvga87rn4up9nVpLUoXJG3Ty5tPjLiLgKGJyZz3W0T9167DGYORPOOguefRZ23bVYP/xwGDq0y4d7/PHy6tlVU5J6V0dTCEXE0xGxTWY+WUrkFrVTb2PgtxTzy97W6tgtrYHLI+JnwJfbiaE+55mVJJWlob0NEfG2iHhDq/XPAJcCJ5dGFBMUc99ddx1MnAhvfCP88Iew335w001w331FM9s6JHkAnf2BduTIYsDOZ54xyZOkPuQKYHJpeTJw+doVImIQ8Gvg/LUHXSklh0QxLPWHgfsrGq0kqSa1m+gBZwArACLiPcCpwPkUwz3Pqnxofdzzz8NPfwpjxsD73188i/eNb8Cjj8Jll8G++75ugvOuaG6GF198ffmQIUVyl1k0IJrgSVKfcyrwvoh4GJhQWicixkXEWaU6nwDeAxwREbNLP2NL25oj4j7gPmAL4Du9G74kqRZ01HVzQGY+W1o+FJiVmb+k6MJZv/P5PPBAMffd+ecXmdjb315kXoccAuuvv86HLWeePLtoSlLfl5lLgPFtlN8JfLa0fCFwYTv771/RACVJdaHDRC8iBmbmSor/sKaUuV/tWbkSrryyGFzlxhuLhO6ww6CxEcaNW+fDtpfctTdP3tChJnmSJEmSOtdRwnYRcEtEPEMxxcKtABGxE0X3zdq3aFExsMrpp8P8+cVDc//1X3D00bDllut0yK4md62VOziLJEmSpPrWbqKXmadExA0Uc+b9PvOfqUgDcHxvBFc1f/5z0Xp3ySWwYgWMHw8/+QkcdBAMXPfGzJZ58VqmTCgnuWvN0bMlSZIklaPDrKX1cM+tyv5WuXCq6JVX4NJLiwTvjjuKfpKf+xxMmwa77NKtQ7duxVtXQ4bAKad0KwxJkiRJdaK+nrVry/z5xVx3Z55ZzFPw5jcXyd6nP11MdN5Na7firQsHYZEkSZLUFfWZ6GXCzTcXCd1vflOUHXxwMbjK/vt3a1qEtU2fXn6S1/LM3oABsGpVMU/eKaeY4EmSJEnqmo7m0as9L7wAp50Gu+1WJHS33AJf/So88gj8+tfFs3g9lOQ1N8OoUZ1312w53ciRcMEFRaK3cqXz5EmSJElad/XRovfQQ8Xcd+eeWyR7b31rsXzooTB4cLcP3/IM3uOPFwOmfPCDcN55nbfk2WInSZIkqRJqN9FbtQp++9uie+Z118F66xWJXWMj7L13j7bctX4Gb968YjaGjkbUHDIEZs0ywZMkSZJUGbWX6C1ZAmefXXTRnDcPttsOvvMd+OxnYeute/x0bT2D11GSZyueJEmSpEqrnUTv7ruL1ruLLiqmSth3X/jhD2HixG7NfdeZrkxiPnJk8dydJEmSJFVSbSR63/wmfOtbRZ/II44o5r7bbbdeOfWIEW0PuNIygmYL58GTJEmS1FtqY9TNgw+G//5vWLiwmBOvl5I8KJK3IUPWLBsyBI49tmjBiyhefSZPkiRJUm+pjRa9vfYqfqqgJXlrPeqmz+BJkiRJqqbaSPSqbNIkEztJkiRJfUdtdN2UJEmSJP2TiZ4kSZIk1RgTPUmSJEmqMSZ6kiRJklRjTPQkSZIkqcaY6EmSJElSjTHRkyRJkqQaY6LXjuZmGDUKGhqK1+bm8rZJkiRJUrU5YXobmpthyhRYtqxYnzevWG/R3jYnTZckSZLUF5jotWH69NcSuRbLlhXlLcttbTPRkyRJktQXmOi14fHHu1be2TZJkiRJ6k0+o9eGESPaL+9omyRJkiT1BSZ6bTjlFBgyZM2yIUOK8o62SZIkSVJfYKLXhkmTYNYsGDkSIorXWbOK8o62SZIUEZtFxHUR8XDpddN26q2KiNmlnytale8QEbdHxNyIuCQiBvVe9JKkWhGZWe0YyjZu3Li88847qx2GJKnCIuKuzBxX7TjWRUR8D3g2M0+NiBOBTTPza23UezEzh7ZRfinwq8y8OCJOB+7JzJkdndP/HyWpfpT7f6QtepIk9ayJwHml5fOAD5e7Y0QEsD9w2brsL0lSCxM9SZJ61taZ+WRp+Slg63bqDY6IOyPitohoSeY2B5Zm5srS+gJguwrGKkmqUU6vIElSF0XE9cAb2tg0vfVKZmZEtPeMxMjMXBgRbwRujIj7gOe6EMMUYArACId+liStxURPkqQuyswJ7W2LiKcjYpvMfDIitgEWtXOMhaXXRyLiZmBP4JfAsIgYWGrV2x5Y2M7+s4BZUDyj153rkSTVHrtuSpLUs64AJpeWJwOXr10hIjaNiPVLy1sA7wIeyGKEtJuAQzraX5KkzpjoSZLUs04F3hcRDwMTSutExLiIOKtUZxfgzoi4hyKxOzUzHyht+xrwxYiYS/HM3tm9Gr0kqSZUtetmRHwJ+AGwZWY+U81YJEnqCZm5BBjfRvmdwGdLy38C3tLO/o8Ae1cyRklS7atai15EDAfeDzxerRgkSZIkqRZVs+vmj4GvAj5ALkmSJEk9qCqJXkRMBBZm5j1l1J1SmmfozsWLF/dCdJIkSZLUv1XsGb1O5hj6BkW3zU45fLQkSZIkdU3FEr325hiKiLcAOwD3RAQUcwTdHRF7Z+ZTlYpHkiRJkupFr4+6mZn3AVu1rEfEY8A4R92UJEmSpJ7hPHqSJEmSVGOqOo8eQGaOqnYMkiRJklRLbNGTJEmSpBpjoidJkiRJNcZET5IkSZJqjImeJEmSJNUYEz1JkiRJqjEmepIkSZJUY0z0JEmSJKnGmOhJkiRJUo0x0ZMkSZKkGmOiJ0mSJEk1xkRPkiRJkmpM3SZ6zc0wahQ0NBSvzc3VjkiSJEmSesbAagdQDc3NMGUKLFtWrM+bV6wDTJpUvbgkSZIkqSfUZYve9OmvJXktli0ryiVJkiSpv6vLRO/xx7tWLkmSJEn9SV0meiNGdK1ckiRJkvqTukz0TjkFhgxZs2zIkKJckiRJkvq7ukz0Jk2CWbNg5EiIKF5nzXIgFkmSJEm1oS5H3YQiqTOxkyRJklSL6rJFT5IkSZJqmYmeJEk9KCI2i4jrIuLh0uumbdTZLyJmt/p5JSI+XNp2bkQ82mrb2N6/CklSf2eiJ0lSzzoRuCEzRwM3lNbXkJk3ZebYzBwL7A8sA37fqspXWrZn5uxeiVqSVFNM9CRJ6lkTgfNKy+cBH+6k/iHANZm5rKJRSZLqiomeJEk9a+vMfLK0/BSwdSf1DwMuWqvslIi4NyJ+HBHr93iEkqSaV7ejbkqStK4i4nrgDW1smt56JTMzIrKD42wDvAW4tlXx1ykSxEHALOBrwLfb2HcKMAVgxIgRXbwCSVKtM9GTJKmLMnNCe9si4umI2CYznywlcos6ONQngF9n5qutjt3SGrg8In4GfLmdGGZRJIKMGzeu3WRSklSf7LopSVLPugKYXFqeDFzeQd1Psla3zVJySEQExfN991cgRklSjTPRkySpZ50KvC8iHgYmlNaJiHERcVZLpYgYBQwHbllr/+aIuA+4D9gC+E4vxCxJqjF23ZQkqQdl5hJgfBvldwKfbbX+GLBdG/X2r2R8kqT6YIueJEmSJNUYEz1JkiRJqjEmepIkSZJUY0z0JEmSJKnGmOhJkiRJUo0x0ZMkSZKkGmOiJ0mSJEk1xkRPkiRJkmqMiZ4kSZIk1RgTPUmSJEmqMSZ6kiRJklRjTPQkSZIkqcaY6EmSJElSjTHRkyRJkqQaU7VELyKOj4i/RsSciPheteKQJEmSpFpTlUQvIvYDJgJ7ZOauwA/W9VjNzTBqFDQ0FK/NzT0UpCRJkiT1UwOrdN7jgFMzczlAZi5al4M0N8OUKbBsWbE+b16xDjBpUo/EKUmSJEn9TrW6br4JeHdE3B4Rt0TE29qrGBFTIuLOiLhz8eLFa2ybPv21JK/FsmVFuSRJkiTVq4q16EXE9cAb2tg0vXTezYC3A28DLo2IN2Zmrl05M2cBswDGjRu3xvbHH2/73O2VS5IkSVI9qFiil5kT2tsWEccBvyoldn+OiNXAFsDi9vZpy4gRRXfNtsolSZIkqV5Vq+vmb4D9ACLiTcAg4JmuHuSUU2DIkDXLhgwpyiVJkiSpXlUr0TsHeGNE3A9cDExuq9tmZyZNglmzYORIiCheZ81yIBZJkiRJ9a0qo25m5grg8J441qRJJnaSJEmS1FrVJkyXJEmSJFWGiZ4kSZIk1RgTPUmSJEmqMSZ6kiRJklRjTPQkSZIkqcaY6EmSJElSjTHRkySpB0XExyNiTkSsjohxHdQ7ICIeioi5EXFiq/IdIuL2UvklETGodyKXJNUSEz1JknrW/cBHgT+0VyEiBgAzgAOBMcAnI2JMafN3gR9n5k7AP4CjKxuuJKkWmehJktSDMvPBzHyok2p7A3Mz85HMXAFcDEyMiAD2By4r1TsP+HDlopUk1aqB1Q6gK+66665nImLeWsVbAM9UI54qq8frrsdrBq+73njdhZHVCqSXbAfMb7W+ANgH2BxYmpkrW5Vv19YBImIKMKW0+mJEdJZclqNe//11xHvyet6T1/OevJ73pG09cV/K+j+yXyV6mbnl2mURcWdmtvsMRK2qx+uux2sGr7vacfQ2r7t/iIjrgTe0sWl6Zl7eGzFk5ixgVk8es7/9HnqD9+T1vCev5z15Pe9J23rzvvSrRE+SpL4gMyd08xALgeGt1rcvlS0BhkXEwFKrXku5JEld4jN6kiT1vjuA0aURNgcBhwFXZGYCNwGHlOpNBnqlhVCSVFtqIdHr0W4r/Ug9Xnc9XjN43fXG6+7nIuIjEbEAeAfw24i4tlS+bURcDVBqrWsErgUeBC7NzDmlQ3wN+GJEzKV4Zu/sXgy/Zn4PPch78nrek9fznrye96RtvXZfovjjoSRJkiSpVtRCi54kSZIkqRUTPUmSJEmqMf0+0YuIkyPi3oiYHRG/j4htqx1Tb4iI70fEX0vX/uuIGFbtmHpDRHw8IuZExOqIqPkheyPigIh4KCLmRsSJ1Y6nN0TEORGxKCLur3YsvSUihkfETRHxQOnf9wnVjqk3RMTgiPhzRNxTuu5vVTumelWPnzWdqdf3ZWciYkBE/CUirqp2LH1FRAyLiMtK38sejIh3VDumaouIfyu9b+6PiIsiYnC1Y+ptbX2fiYjNIuK6iHi49LppJWPo94ke8P3M3D0zxwJXASdVO6Bech2wW2buDvwN+HqV4+kt9wMfBf5Q7UAqLSIGADOAA4ExwCcjYkx1o+oV5wIHVDuIXrYS+FJmjgHeDkyrk9/1cmD/zNwDGAscEBFvr3JMdaeOP2s6U6/vy86cQDGAkF7zP8DvMvPNwB7U+f2JiO2AzwPjMnM3YADFyML15lxe/33mROCGzBwN3FBar5h+n+hl5vOtVjcE6mJ0mcz8fWnUNoDbKOZaqnmZ+WBmPlTtOHrJ3sDczHwkM1cAFwMTqxxTxWXmH4Bnqx1Hb8rMJzPz7tLyCxRfErarblSVl4UXS6vrlX7q4jO8j6nLz5rO1Ov7siMRsT3wr8BZ1Y6lr4iITYD3UBodNzNXZObS6kbVJwwENoiIgcAQ4Ikqx9Pr2vk+MxE4r7R8HvDhSsbQ7xM9gIg4JSLmA5Oonxa91o4Crql2EOpx2wHzW60voM6/ZNSDiBgF7AncXt1IekepG9hsYBFwXWbWxXX3MX7WdKLe3pcd+G/gq8DqagfSh+wALAZ+VurSelZEbFjtoKopMxcCPwAeB54EnsvM31c3qj5j68x8srT8FLB1JU/WLxK9iLi+1Md37Z+JAJk5PTOHA80U8xLVhM6uu1RnOkX3kubqRdqzyrluqRZFxFDgl8AX1uqtULMyc1Wp6/32wN4RsVu1Y5Jaq8f3ZVsi4iBgUWbeVe1Y+piBwF7AzMzcE3iJCnfH6+tKz51NpEiCtwU2jIjDqxtV35PFHHcV7cUysJIH7ymZOaHMqs3A1cB/VDCcXtPZdUfEEcBBwPisoQkRu/D7rnULgeGt1rcvlakGRcR6FF8mmzPzV9WOp7dl5tKIuInieYa6GYinj/Czph31/r5cy7uAgyPig8BgYOOIuDAz6/0L/AJgQaveCJdR54keMAF4NDMXA0TEr4B3AhdWNaq+4emI2CYzn4yIbSh6s1RMv2jR60hEjG61OhH4a7Vi6U0RcQBF94mDM3NZteNRRdwBjI6IHSJiEMWDzFdUOSZVQEQExfMdD2bmj6odT2+JiC1bRgyOiA2A91Enn+F9jJ81bajX92V7MvPrmbl9Zo6i+Ddyo0keZOZTwPyI2LlUNB54oIoh9QWPA2+PiCGl99F46nyAmlauACaXlicDl1fyZP0+0QNOLXXruxd4P8VoUPWgCdgIuC6KqSVOr3ZAvSEiPhIRC4B3AL+NiGurHVOllAbbaQSupfiAvDQz51Q3qsqLiIuA/wN2jogFEXF0tWPqBe8CPg3sX3o/zy791bzWbQPcVPr8voPiGT2HbO9l9fpZU4Z6fV+q644HmkufZWOB/6xyPFVVat28DLgbuI8i35hV1aCqoJ3vM6cC74uIhylaPk+taAw11ONPkiRJkkRttOhJkiRJklox0ZMkSZKkGmOiJ0mSJEk1xkRPkiRJkmqMiZ4kSZIk1RgTPdWUiNi81TDYT0XEwtLy0ojo1XltImJs66G4I+LgiFinSVQj4rGI2KKN8k0i4vyImBsRf4+I5ojYtDtxt3P+dq8lIr4ZEV/u6XNKkiRp3ZnoqaZk5pLMHJuZY4HTgR+XlscCq3v6fBExsIPNY4F/JkeZeUVm9vR8KWcDj2TmTpm5IzAXOLeHzwG9cy2SJEnqISZ6qicDIuLMiJgTEb+PiA0AImLHiPhdRNwVEbdGxJtL5aMi4saIuDciboiIEaXycyPi9Ii4HfheRGwYEedExJ8j4i8RMTEiBgHfBg4ttSgeGhFHRERT6RhbR8SvI+Ke0s87S+W/KcUxJyKmdHQxEbET8Fbg5FbF3wb2iIidI2LfiLiqVf2miDiitHxSRNwREfdHxKyIiFL5zRHx3dK1/C0i3t3ZtawVU3v38uOlc90TEX/o+q9OkiRJXWGip3oyGpiRmbsCS4GPlcpnAcdn5luBLwOnlcp/CpyXmbsDzcBPWh1re+CdmflFYDpwY2buDewHfB9YDzgJuKTUwnjJWrH8BLglM/cA9gLmlMqPKsUxDvh8RGzewfWMAWZn5qqWgtLyX4BdOrkXTZn5tszcDdgAOKjVtoGla/kC8B+ZuaKTa2mtvXt5EvCB0vUe3ElskiRJ6qaOup1JtebRzJxdWr4LGBURQ4F3Ar8oNWoBrF96fQfw0dLyBcD3Wh3rF60SrPcDB7d6Tm0wMKKTWPYHPgP/TM6eK5V/PiI+UloeTpGcLinv8rpkv4j4KjAE2Iwi0byytO1Xpde7gFHlHrCTe/lH4NyIuLTV8SVJklQhJnqqJ8tbLa+iaMlqAJaWnuPripdaLQfwscx8qHWFiNinKweMiH2BCcA7MnNZRNxMkTS25wFgbEQ0ZObq0jEagD2AuymSzdat9oNLdQZTtLSNy8z5EfHNtc7Tcp9W0bXPiHbvZWYeW7of/wrcFRFvzcxKJLCSJEnCrpuqc5n5PPBoRHwcIAp7lDb/CTistDwJuLWdw1wLHN/qObc9S+UvABu1s88NwHGl+gMiYhNgE+AfpSTvzcDbO4l9LkU3zX9vVfzvwA2Z+TgwDxgTEetHxDBgfKlOS1L3TKkV7pCOzlPGtbTE0+69jIgdM/P2zDwJWEzRWilJkqQKMdGTiiTu6Ii4h6IL48RS+fHAkRFxL/Bp4IR29j+Z4pm8eyNiDq8NjnITRaI1OyIOXWufEyi6T95H0UVyDPA7YGBEPAicCtxWRuxHAaNLUysspkgOjwXIzPnApcD9pde/lMqXAmeWyq8F7ijjPB1dS2vt3cvvR8R9EXE/RQJ9TxnnlCRJ0jqKzKx2DJJ6QETsDPwW+HxmXl3teCRJklQ9JnqSJEmSVGPsuilJkiRJNcZET5IkSZJqjImeJEmSJNUYEz1JkiRJqjEmepIkSZJUY0z0JEmSJKnG/H8S0UHVBtYKCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot_diagnostics(figsize=(15, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T08:01:39.096545Z",
     "start_time": "2018-10-28T08:01:39.085048Z"
    }
   },
   "outputs": [],
   "source": [
    "results.get_prediction?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:35:00</th>\n",
       "      <td>2402.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:40:00</th>\n",
       "      <td>2371.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:45:00</th>\n",
       "      <td>2348.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:50:00</th>\n",
       "      <td>2336.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:55:00</th>\n",
       "      <td>2316.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-10-27 23:35:00  2402.19\n",
       "2018-10-27 23:40:00  2371.37\n",
       "2018-10-27 23:45:00  2348.32\n",
       "2018-10-27 23:50:00  2336.29\n",
       "2018-10-27 23:55:00  2316.86"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T08:02:39.643614Z",
     "start_time": "2018-10-28T08:02:24.231616Z"
    }
   },
   "outputs": [],
   "source": [
    "pred = loaded.get_prediction(start=pd.to_datetime('2018-10-28 00:00:00'), end=pd.to_datetime('2018-10-28 23:00:00'), dynamic=False)\n",
    "pred_ci = pred.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T08:02:57.086119Z",
     "start_time": "2018-10-28T08:02:57.077145Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.predicted_mean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['27/10/2018'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(24), data['27/10/2018'])\n",
    "plt.plot(range(24), pred.predicted_mean)#.plot(figsize=(15,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred.predicted_mean.to_csv('arima_pred.csv', index_label='datetime', header=['load'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <td>load</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 00:00:00</th>\n",
       "      <td>3742.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 01:00:00</th>\n",
       "      <td>3496.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 02:00:00</th>\n",
       "      <td>3329.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 03:00:00</th>\n",
       "      <td>3197.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 04:00:00</th>\n",
       "      <td>3101.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 05:00:00</th>\n",
       "      <td>3078.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 06:00:00</th>\n",
       "      <td>3285.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 07:00:00</th>\n",
       "      <td>3494.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 08:00:00</th>\n",
       "      <td>3562.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 09:00:00</th>\n",
       "      <td>3789.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 10:00:00</th>\n",
       "      <td>4234.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 11:00:00</th>\n",
       "      <td>4393.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 12:00:00</th>\n",
       "      <td>4399.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 13:00:00</th>\n",
       "      <td>4276.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 14:00:00</th>\n",
       "      <td>4194.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 15:00:00</th>\n",
       "      <td>4371.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 16:00:00</th>\n",
       "      <td>4345.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 17:00:00</th>\n",
       "      <td>4324.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 18:00:00</th>\n",
       "      <td>4305.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 19:00:00</th>\n",
       "      <td>4374.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 20:00:00</th>\n",
       "      <td>4236.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 21:00:00</th>\n",
       "      <td>4040.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 22:00:00</th>\n",
       "      <td>4064.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 23:00:00</th>\n",
       "      <td>4109.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:00:00</th>\n",
       "      <td>3942.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 01:00:00</th>\n",
       "      <td>3735.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 02:00:00</th>\n",
       "      <td>3561.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 03:00:00</th>\n",
       "      <td>3392.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 04:00:00</th>\n",
       "      <td>3275.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-26 18:00:00</th>\n",
       "      <td>3675.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-26 19:00:00</th>\n",
       "      <td>3657.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-26 20:00:00</th>\n",
       "      <td>3365.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-26 21:00:00</th>\n",
       "      <td>3171.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-26 22:00:00</th>\n",
       "      <td>2963.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-26 23:00:00</th>\n",
       "      <td>2791.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 00:00:00</th>\n",
       "      <td>2567.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 01:00:00</th>\n",
       "      <td>2395.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 02:00:00</th>\n",
       "      <td>2302.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 03:00:00</th>\n",
       "      <td>2218.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 04:00:00</th>\n",
       "      <td>2242.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 05:00:00</th>\n",
       "      <td>2351.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 06:00:00</th>\n",
       "      <td>2492.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 07:00:00</th>\n",
       "      <td>2749.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 08:00:00</th>\n",
       "      <td>2937.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 09:00:00</th>\n",
       "      <td>3069.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 10:00:00</th>\n",
       "      <td>3255.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 11:00:00</th>\n",
       "      <td>3405.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 12:00:00</th>\n",
       "      <td>3389.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 13:00:00</th>\n",
       "      <td>3242.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 14:00:00</th>\n",
       "      <td>3131.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 15:00:00</th>\n",
       "      <td>3197.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 16:00:00</th>\n",
       "      <td>3097.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 17:00:00</th>\n",
       "      <td>3224.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 18:00:00</th>\n",
       "      <td>3451.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 19:00:00</th>\n",
       "      <td>3373.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 20:00:00</th>\n",
       "      <td>3123.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 21:00:00</th>\n",
       "      <td>2854.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 22:00:00</th>\n",
       "      <td>2745.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:00:00</th>\n",
       "      <td>2546.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>721 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "datetime                load\n",
       "2018-09-28 00:00:00  3742.89\n",
       "2018-09-28 01:00:00  3496.01\n",
       "2018-09-28 02:00:00  3329.21\n",
       "2018-09-28 03:00:00  3197.15\n",
       "2018-09-28 04:00:00  3101.24\n",
       "2018-09-28 05:00:00  3078.06\n",
       "2018-09-28 06:00:00  3285.45\n",
       "2018-09-28 07:00:00  3494.94\n",
       "2018-09-28 08:00:00   3562.9\n",
       "2018-09-28 09:00:00  3789.46\n",
       "2018-09-28 10:00:00  4234.12\n",
       "2018-09-28 11:00:00  4393.12\n",
       "2018-09-28 12:00:00  4399.11\n",
       "2018-09-28 13:00:00  4276.69\n",
       "2018-09-28 14:00:00  4194.43\n",
       "2018-09-28 15:00:00  4371.21\n",
       "2018-09-28 16:00:00  4345.13\n",
       "2018-09-28 17:00:00  4324.12\n",
       "2018-09-28 18:00:00  4305.82\n",
       "2018-09-28 19:00:00  4374.61\n",
       "2018-09-28 20:00:00  4236.76\n",
       "2018-09-28 21:00:00  4040.98\n",
       "2018-09-28 22:00:00  4064.04\n",
       "2018-09-28 23:00:00  4109.75\n",
       "2018-09-29 00:00:00  3942.27\n",
       "2018-09-29 01:00:00  3735.92\n",
       "2018-09-29 02:00:00  3561.55\n",
       "2018-09-29 03:00:00  3392.04\n",
       "2018-09-29 04:00:00  3275.89\n",
       "...                      ...\n",
       "2018-10-26 18:00:00  3675.67\n",
       "2018-10-26 19:00:00   3657.7\n",
       "2018-10-26 20:00:00  3365.28\n",
       "2018-10-26 21:00:00  3171.04\n",
       "2018-10-26 22:00:00  2963.51\n",
       "2018-10-26 23:00:00   2791.7\n",
       "2018-10-27 00:00:00  2567.64\n",
       "2018-10-27 01:00:00  2395.57\n",
       "2018-10-27 02:00:00  2302.61\n",
       "2018-10-27 03:00:00   2218.4\n",
       "2018-10-27 04:00:00  2242.06\n",
       "2018-10-27 05:00:00  2351.67\n",
       "2018-10-27 06:00:00  2492.24\n",
       "2018-10-27 07:00:00  2749.18\n",
       "2018-10-27 08:00:00  2937.47\n",
       "2018-10-27 09:00:00  3069.36\n",
       "2018-10-27 10:00:00  3255.88\n",
       "2018-10-27 11:00:00  3405.86\n",
       "2018-10-27 12:00:00  3389.34\n",
       "2018-10-27 13:00:00  3242.79\n",
       "2018-10-27 14:00:00  3131.61\n",
       "2018-10-27 15:00:00  3197.41\n",
       "2018-10-27 16:00:00  3097.94\n",
       "2018-10-27 17:00:00  3224.37\n",
       "2018-10-27 18:00:00  3451.46\n",
       "2018-10-27 19:00:00  3373.16\n",
       "2018-10-27 20:00:00  3123.26\n",
       "2018-10-27 21:00:00  2854.02\n",
       "2018-10-27 22:00:00  2745.78\n",
       "2018-10-27 23:00:00  2546.12\n",
       "\n",
       "[721 rows x 1 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "day = datetime.today() - timedelta(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(744, 1)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[day.strftime('%d/%m/%Y'):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('monthdata.csv', columns=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:59:44.487615Z",
     "start_time": "2018-10-28T07:59:44.463378Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'delhi' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-2ec3816395c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdelhi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'delhi' is not defined"
     ]
    }
   ],
   "source": [
    "delhi.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T08:00:26.474696Z",
     "start_time": "2018-10-28T08:00:25.916103Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8938658128>]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(delhi['2018-08-21'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T08:00:39.761880Z",
     "start_time": "2018-10-28T08:00:39.747247Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-08-22    5649.586455\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.predicted_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T08:03:18.619611Z",
     "start_time": "2018-10-28T08:03:18.598459Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T08:03:50.328933Z",
     "start_time": "2018-10-28T08:03:49.872106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(24), delhi['2018-08-21'])\n",
    "plt.plot(range(24), pred.predicted_mean)#.plot(figsize=(15,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T05:56:39.492625Z",
     "start_time": "2018-10-28T05:56:38.912156Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:06:14.330143Z",
     "start_time": "2018-10-28T06:06:13.821443Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = delhi['2018-08-21':].plot(figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:06:21.623806Z",
     "start_time": "2018-10-28T06:06:21.613240Z"
    }
   },
   "outputs": [],
   "source": [
    "pr = pred.predicted_mean\n",
    "axr = delhi['2018-08-21':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(axr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ax = delhi['2018-08-01':'2018-08-02'].plot(label='observed', figsize=(15,6))\n",
    "# pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7)\n",
    "\n",
    "# # ax.fill_between(pred_ci.index,\n",
    "# #                 pred_ci.iloc[:, 0],\n",
    "# #                 pred_ci.iloc[:, 1], color='k', alpha=.2)\n",
    "\n",
    "# ax.set_xlabel('Date')\n",
    "# ax.set_ylabel('Electricity Production')\n",
    "# plt.legend()\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# type(pr), type(axr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T05:59:24.079358Z",
     "start_time": "2018-10-28T05:59:24.025983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "2018-08-21 00:00:00   -5431.196736\n",
       "2018-08-21 01:00:00   -5036.413804\n",
       "2018-08-21 02:00:00   -4739.511659\n",
       "2018-08-21 03:00:00   -4483.035169\n",
       "2018-08-21 04:00:00   -4286.424075\n",
       "2018-08-21 05:00:00   -4122.607810\n",
       "2018-08-21 06:00:00   -4043.337895\n",
       "2018-08-21 07:00:00   -4040.936234\n",
       "2018-08-21 08:00:00   -4107.953184\n",
       "2018-08-21 09:00:00   -4297.729721\n",
       "2018-08-21 10:00:00   -4896.561318\n",
       "2018-08-21 11:00:00   -5106.376316\n",
       "2018-08-21 12:00:00   -5256.390262\n",
       "2018-08-21 13:00:00   -5214.683170\n",
       "2018-08-21 14:00:00   -5423.418943\n",
       "2018-08-21 15:00:00   -5782.623318\n",
       "2018-08-21 16:00:00   -5716.536227\n",
       "2018-08-21 17:00:00   -5514.991903\n",
       "2018-08-21 18:00:00   -5173.711180\n",
       "2018-08-21 19:00:00   -5037.278554\n",
       "2018-08-21 20:00:00   -5268.770248\n",
       "2018-08-21 21:00:00   -5250.630262\n",
       "2018-08-21 22:00:00   -5635.268161\n",
       "2018-08-21 23:00:00   -5856.386482\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.predicted_mean - numpy.squeeze(delhi['2018-08-21':])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-21 00:00:00</th>\n",
       "      <td>5425.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 00:30:00</th>\n",
       "      <td>5185.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 01:00:00</th>\n",
       "      <td>5031.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 01:30:00</th>\n",
       "      <td>4881.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 02:00:00</th>\n",
       "      <td>4734.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-08-21 00:00:00  5425.76\n",
       "2018-08-21 00:30:00  5185.79\n",
       "2018-08-21 01:00:00  5031.29\n",
       "2018-08-21 01:30:00  4881.22\n",
       "2018-08-21 02:00:00  4734.64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T05:59:31.115609Z",
     "start_time": "2018-10-28T05:59:31.105222Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5021.457622825617\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt \n",
    "print(sqrt(((pred.predicted_mean - numpy.squeeze(delhi['2018-08-21':]))**2).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T05:59:52.612480Z",
     "start_time": "2018-10-28T05:59:52.601336Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Squared Error of our forecasts is 25215036.66\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "y_forecasted = pred.predicted_mean\n",
    "y_truth = delhi['2018-08-21':]\n",
    "# print(y_truth.values)\n",
    "# Compute the mean square error\n",
    "mse = ((numpy.squeeze(y_forecasted.values) - numpy.squeeze(y_truth.values)) ** 2).mean()\n",
    "print('The Mean Squared Error of our forecasts is {}'.format(round(mse, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hlwa = results.get_forecast(480)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delhi = delhi.asfreq(freq='300S', method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:00:14.403428Z",
     "start_time": "2018-10-28T06:00:14.372286Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get forecast 500 steps ahead in future\n",
    "pred_uc = results.get_forecast(steps = 48)\n",
    "\n",
    "# Get confidence intervals of forecasts\n",
    "pred_ci = pred_uc.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:01:11.242838Z",
     "start_time": "2018-10-28T06:01:11.220006Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lower load</th>\n",
       "      <th>upper load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-22 00:00:00</th>\n",
       "      <td>-9439.959146</td>\n",
       "      <td>9428.544697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-22 01:00:00</th>\n",
       "      <td>-9439.544299</td>\n",
       "      <td>9428.959543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-22 02:00:00</th>\n",
       "      <td>-9439.232308</td>\n",
       "      <td>9429.271535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-22 03:00:00</th>\n",
       "      <td>-9438.962796</td>\n",
       "      <td>9429.541046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-22 04:00:00</th>\n",
       "      <td>-9438.756193</td>\n",
       "      <td>9429.747650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      lower load   upper load\n",
       "2018-08-22 00:00:00 -9439.959146  9428.544697\n",
       "2018-08-22 01:00:00 -9439.544299  9428.959543\n",
       "2018-08-22 02:00:00 -9439.232308  9429.271535\n",
       "2018-08-22 03:00:00 -9438.962796  9429.541046\n",
       "2018-08-22 04:00:00 -9438.756193  9429.747650"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_ci.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:01:46.495650Z",
     "start_time": "2018-10-28T06:01:46.487079Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 2)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_ci.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T06:02:14.582295Z",
     "start_time": "2018-10-28T06:02:13.961753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f893871dc50>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_ci.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f87f1effd68>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zlZBAP4YlNnE6lkiVST+Qy8zVmdzRO4bw4HO3HYlU3Nksy7wL+Ju1thDAWrvPNT4U+NxaW2it3QakAd1clzRr7VZrbRHwuetYjzC6byz7jhQybcUup6OIiIi4naOFJYyemExwgC9v3dAZ/2reYt3P14c3r0+gd4u6PDp1DfNS9zodSaTKvLtgK34+PtxxfnOno8hplPed0wI/GGOSjTEjXWMtgd7GmKXGmAXGmK6u8Shg53H3zXCNnWz8d4wxI40xScaYpKysrIq8Fkf1iqtD+6havPfzVkrVIllERKTcrLU8OnUN2/bn8ub1CTQIC3I6UrkE+vky/uYutI8K477PV7J+92GnI4lUuj05BUxNzmBYYmPq13KPf5verLzFXS9rbWdgCHC3MaYPx5Z0RgA9gD8BX5pjfYpP1KvYnmL89wPWvmetTbTWJtarV6+c8ZxnjGF031i27s/lh3V7nI4jIiLiNiYs2s53a3bz8EWtOC+2rtNxKiQ4wI/3b0kkNMifOz5OIutIodORRCrVB79spdRaRvWJdTqKlEO5ijtrbabr5z5gGseWWGYAX9tjlgFlQF3X+PELzxsDmacY9xhD2jekaZ1g3l2wRe2RRUREyiE5/SAvfreegW0iGe2mHx4jawXxwYhEDuQWMurTJAqKS52OJFIpDuUWMXnZDi6Pb0R0nWCn40g5nLa4M8aEGGNCf7sODALWAtOBC1zjLYEAYD8wAxhujAk0xjQHWgDLgOVAC2NMc2NMAMearsyo/JfkHF8fw529Y1idkcPirQecjiMiIlKtFZeW8dCXq2kUXoNXr43Hx8fZjcrPRvuoMF6/rhMrdmTz6NQ1+pJXPMKERdvJKyrlrn7u+cWLNyrPzF0ksNAYs5pjRdp31trZwL+AGGPMWo41RxnhmsVbB3wJpAKzgbuttaXW2hLgHmAOsB740nWsR7mmS2Pq1gzg3QVbnY4iIiJSrX22bAfbD+Txl8vbElbD3+k4Z21w+4b86aJWfLMqk7d/SnM6jshZOVpYwoRF27mwbSQtI0OdjiPldNqtEKy1W4H4E4wXATed5D4vAi+eYPx74PuKx3QfQf6+3NqrOS/P2ci6zBzaNQpzOpKIiEi1c7SwhDd/3Ey35rXp36q+03EqzZh+saTtO8orP2wipl5NLu5Q/fbqEymPyUvTyckvZoxm7dxK9e4z7KZu6tGUmoF+jNfsnYiIyAm9//NW9h8t4vEhrTnWj80zGGN46aoOdGkawYNfriIlI8fpSCIVVlBcyvu/bKNXXB0SoiOcjiMVoOKuCoTV8OeG7tF8uyaTnQfznI4jIiJSrWQdKeT9X7YypH0Dj/zgGOR/bIuEOiGB3PHJcvbkFDgdSaRCpiRnkHWkkLv7xTkdRSpIxV0Vua1Xc3x9DO//otk7ERGR473542YKS8r400WtnI5SZerWDOTDPyRytKCEOz9JIr9IHTTFPZSUljH+5y10ahJOz9g6TseRClJxV0UahAVxZUIUXyzfyf6j2vNGREQEYNv+XD5btoPhXZsQU6+m03GqVOsGtXjz+gTWZubw0FerKCtTB02p/r5ds5udB/O5u3+cRy2Z9hYq7qrQyD6xFJWW8fGi7U5HERERqRZembMRf18f7hvYwuko58SANpH8+eI2fJ+yh3/M2+R0HJFTKiuzvDM/jZaRNRnQ2nMaHXkTFXdVKK5+TS5sE8kni9PJLSxxOo6IiIijVu3M5ruU3dzZuzn1Q4OcjnPO3H5+c65LbMI//53GN6t2OR1H5KTmrd/Lpr1HGdMvzq33nfRmKu6q2Oh+seTkF/PZsh1ORxEREXGMtZa/zVpPnZAA7uwT43Scc8oYw/NXtKd789r8acoaktMPOR1J5H9Ya3l7/haa1K7BpR21hYe7UnFXxTpHR9CteW0+XLiNopIyp+OIiIg4Yv6mLJZsPcgfL4gjNMj9NyyvqAA/H969qQsNw4IY9WkSGYfUTVuql0VbDrB6Zzaj+8bi56sSwV3pN3cO3NU3lt05BcxYnel0FBERkXOutMzy91kbiK4dzA3dmzodxzERIQF8OKIrhSVl3PFxEkd1yoZUI2//lEb90ECu7tzY6ShyFlTcnQP9WtWjdYNQxi/Yok5ZIiLidaav3MWGPUd4+KJWBPh590ePuPo1efuGzmzed5T7P19FqT4XSDXw4/q9LNpygDt7xxDk7+t0HDkL3v0Oe44YYxjdN5bN+47y7w37nI4jIiJyzhQUl/La3E10iArj0g46jwegT8t6PHNZW+at38vY2RucjiNeLievmMe/TqF1g1BGnNfM6ThyllTcnSOXdmxIVHgNxi3Y4nQUERGRc+bTxensys7nsSGt1X3vOLf0bMYtPZsy/uetfJm00+k44sWe/XYdB3KLeGVYvNfPrHsC/QbPET9fH+7s3Zzk9EMs337Q6TgiIiJVLie/mLd+SqNPy3r0iqvrdJxq5+lL29K7RV3+PC1Fnw3EEfNS9/L1il2M6RdL+6gwp+NIJVBxdw5d27UJEcH+vDtfs3ciIuL5xs3fwuGCYh4d3MrpKNWSn68Pb93QmajwGjzwxSo1WJFzKievmCemHVuO+ccLWjgdRyqJirtzKDjAjxHnNePHDfvYuOeI03FERESqzO6cfD76dRtXdIqiXSPNCJxMWA1/XhkWz67sfF76fr3TccSLaDmmZ9Jv8hwb0bMZNQP9ePH79VirDlkiIuKZ/jF3E9bCgxe2dDpKtZfYrDZ3nN+cSUt3sHDzfqfjiBf4bTnm3VqO6XFU3J1jESEBPHhhS37elMXstXucjiMiIlLpNu09wpTkDG7u2ZQmtYOdjuMWHhrUiph6ITw6dQ1HCoqdjiMe7PjlmPdoOabHUXHngFt6NqVNw1o8OzNV6+tFRMTjjJ29gZAAP+7uH+d0FLcR5O/LK8Pi2Z2Tz1+1PFOq0LMztRzTk+k36gA/Xx9euKI9ew4X8OaPm52OIyIiUmmWbTvIvPX7GN0vltohAU7HcSudoyO4s08Mny3byYJNWU7HEQ80L3UvX6/UckxPpuLOIV2aRjC8axM+XLhNzVVERMQjWGt5adZ6ImsFcluv5k7HcUsPDGxJXP2aPDplDTn5Wp4plUfLMb2DijsHPTq4NbWC/Hhyeoqaq4iIiNubs24vK3dk88DAltQI8HU6jlsK8vfl1WHxZB0t5IVvU52OIx5EyzG9g36zDooICeCxIa1Zvv0QU1fscjqOiIjIGSspLWPsnA3E1gvhmi6NnY7j1uKbhDO6bwxfJWfw7w17nY4jHkDLMb2HijuHDevShM7R4bz0/Xqy84qcjiMiInJGvkzKYGtWLo8Obo2frz5enK17B7SgVWQoj01NISdPyzPlzGXnFfG4lmN6Db37OszHx/DCFR04lFfE2DkbnY4jIiJSYXlFJfxj3ia6NI3gwraRTsfxCIF+vrx6bTwHcot4duY6p+OIG3tuZioHtRzTa+g3XA20bVSLP5zXnM+W7WDljkNOxxEREamQfy3cRtaRQh4f0hpjjNNxPEb7qDDu7h/H1yt38cM67Y0rFaflmN5HxV018cCFLahXM5Anp6+ltEzNVURExD0cOFrIuwu2cmHbSBKb1XY6jse5p38cbRrW4olpazmUq9M3pPy0HNM7qbirJkKD/Hnq0rasyzzMxCXpTscREREpl7d+SiOvqIRHB7dyOopHCvDz4dVh8WTnFfHMDC3PlPJ7dmYqh7Qc0+voN12NXNqxIefH1eWVORvZd6TA6TgiIiKnlH4gl4lL0rk2sQlx9UOdjuOx2jaqxb0DWjBjdSazUnY7HUfcwNzUvUxbuYsx/eO0HNPLlKu4M8ZsN8akGGNWGWOS/uvPHjbGWGNMXddtY4x50xiTZoxZY4zpfNyxI4wxm12XEZX7UtyfMYbnhrajsKSMv3633uk4IiIipzR29kb8fHx48MKWTkfxeHf1i6V9VC2enL6WA0cLnY4j1Vh2XtH/b1beP87pOHKOVWTmrr+1tpO1NvG3AWNME+BCYMdxxw0BWrguI4FxrmNrA88A3YFuwDPGmIizi+95YurVZHTfGKavymTRlv1OxxERETmh5PRDfJeym1F9Y6hfK8jpOB7P39eHV4d14nBBMU9/o+WZcnJajundzvY3/g/gEeD4DiBDgU/sMUuAcGNMQ+AiYK619qC19hAwFxh8ls/vkcb0j6NJ7Ro8NX0tRSVlTscRERH5HWstL36XSv3QQEb2iXE6jtdo1SCU+we25LuU3Xy7JtPpOFINaTmmlLe4s8APxphkY8xIAGPM5cAua+3q/zo2Cth53O0M19jJxuW/BPn78tzl7dmSlcsHC7c6HUdEROR3Zq3dw4od2Tw0qCXBAX5Ox/Eqo/rEEN84jKemryXriJZnyv/TckyB8hd3vay1nTm25PJuY0wf4M/A0yc49kQb3NhTjP/+zsaMNMYkGWOSsrKyyhnP8/RvXZ+L2kXy5o+byTiU53QcERERAIpKyvjbrA20igzlmi5NnI7jdfx8fXhlWDy5RaU8OT0Fa7V9khybTddyTAEo19dt1tpM1899xphpQF+gObDatVlpY2CFMaYbx2bkjn+3bwxkusb7/df4/BM813vAewCJiYle/Y719GXtGPjqAp6dmcr7tySe/g4iIiJV7NMl6ew4mMfHt3XD10cbljuhRWQoD13YkpdmbWDG6kyGdtJCKE9SXFpGdl4xOflFHMor5lBuEdl5xWS7bmfnFXEot5hDeUXk5B/7eSivmKKSMu4d0ELLMb3caYs7Y0wI4GOtPeK6Pgh4zlpb/7hjtgOJ1tr9xpgZwD3GmM851jwlx1q72xgzB/jrcU1UBgGPV/Lr8ShR4TW4b2AL/jZrA/NS9zKwbaTTkUREpBKt2HGIDbuPcG1iY/x8q/837Tl5xbz542Z6t6hL35b1nI7j1e7oHcPsdXt4+pt19Iypo6Y2biw18zATl6azcPN+DuUWcaSw5KTH+vkYwoMDiAj2JyI4gOjawXRsHEZEcABNagdzXVfNpnu78szcRQLTXDN0fsBka+3sUxz/PXAxkAbkAbcCWGsPGmOeB5a7jnvOWnvwTIN7i9t6NWdqcgZ/mbmOXnF1qRHg63QkERE5S9v25zJ29gZmrd0DwJTknbwxPIEmtYMdTnZqb/20mcMFxTw+pI3TUbyer4/hlWHxXPzGLzwxLYX3b0nE9VlN3EBBcSnfp+xm4pJ0VuzIJtDPh/6t6tMgLIiI4AAiQvwJq3GsgIsIDiA82J+IkABCAnz1e5ZTMtV5rXZiYqJNSko6/YEebsnWAwx/bwn39I/j4YtaOR1HRETO0IGjhfzz32lMXJJOgJ8Po/rE0jiiBn+ZuQ5r4fkr2nFlQmOnY57QzoN5DHh1AUM7NeLlYfFOxxGXD37ZygvfredvV3VgeLdop+PIaWzfn8vkZTv4Kmknh/KKiakbwg3do7mmS2PCgwOcjifVlDEm+fjt6E5FLa7cQI+YOlyVEMX4n7dwRUIUcfVrOh1JREQqoKC4lA8XbuPd+VvIKy5leNcm3D+wJfVCAwHoHlObB75YxQNfrGb+xiyev6I9tYL8HU79e2PnbMTHBx4apC8Zq5NbezXnx/X7eHxaCgfzirirb6xmdqqZktIyftywj4lL0vll8358fQyD2kZyU4+mnBdbR78vqVSauXMTWUcKueDV+XSICmPSHd31RiAi4gZKyyzTVu7i1R82sjungIFtInlsSCvi6oee8Nh3fkrj9R8306BWEG8M70Ris9oOpP5fK3cc4sp3FnHvBXE8qOKu2ikoLuVPU9Ywc3UmVyVE8derOhDkr9M4nLb3cAGfLdvB58t2sudwAQ1qBXF9t2iGd2tCpM6RlAqoyMydijs38uni7Tz1zTreGN5JnbFERKq5nzdl8dKsDazffZj4xmE8cXEbusfUOe39Vuw4xP2fryLjUB73XNCCey+Ic7TZirWWa8cvZtv+POb/qR81A7Xopzqy1vLWv9N4de4mOkeHM/7mxP/MDMu5Y61l0ZYDTFySzg+peykts/RuUZebejRlQOv6btE4SaofFXceqrTMcuU7v7I7p4AfH+pb7ZbsiIjIsc53L81azy+b99Okdg0euag1l3RoiE8Ftg04UlDMX2akMnX0vFXwAAAgAElEQVRFBgnR4bxxXQLRdZxptjJ77R5GT0zmr1d24IbuOqerupuVspsHvlxFnZBA3r8lkbaNajkdySscLijmy+U7mbx0B1v35xIe7M+1iU24oVs0zeqGOB1P3JyKOw+2JiOboW//ys09mvLc0PZOxxEREZfM7Hxe/WETX6/MoFaQP3+8II6bezYl0O/Ml8fNXJ3JE9NSsBaeG9qOKxOizumy/KKSMgb9YwH+vj7Muq+3Zh3cxNpdOdzxcRKHC4p5/bpODGrXwOlIHm1Xdj43fbCUbftz6Rwdzk09mnJxh4ZaGiuVRg1VPFjHxuGM6NmMCYu2M6R9Q3rGnn6Jj4iIVJ3DBcWMm7+Ffy3chgVG9o5hTL84woLPfnXFZfGNSIgO58EvVvPgl//fbCWsxrlZuTF5aTrbD+Tx0R+6qrBzI+2jwphxTy/u/CSJUROTeeSi1ozuG6Pz9avA9v253PjBUg4XFPPZnT30uUwcp5k7N5RXVMLFb/xCSZllzv19CNH5DyIijpiSnMFfv1/PwdwirujUiIcvakXjiMpfPllaZhk3P41/zDvWbOX14Z3oWsXNVnLyi+n38k+0bVSLiberkZc7+l2jlc5RvHRVh7OaSZbf27jnCDd9uJTSMssnt3WjfVSY05HEQ1Vk5k5fw7mh4AA/Xh4Wz67sfF6atd7pOCIiXqektIy/zFjHw1+tJq5eTWbecz6vD0+oksIOjm1Yfc8FLZgyuid+vobrxi/mtR82UlJaViXPB/DO/DSy84t54uI2KuzcVJC/L28O78SDF7bk6xW7uOH9pew/Wuh0LI+QkpHDde8txgBfjOyhwk6qDRV3bqprs9rc3qs5E5fsYOHm/U7HERHxGjl5xfzho+VMWLSd289vzuQ7u9Oh8bn5YJcQHcF39/bmqs6NefPfaQwbv5j0A7mV/jw7D+bx0a/buSqhMe0a6UOrOzPGcO+AFrxzY2fWZeYw9K1fWb/7sNOx3Nry7Qe54f0l1Az0Y8ro82gR+b9bm4g4RcWdG3v4olbE1A3h0alrOFJQ7HQcERGPl7bvKFe88ytLtx1g7NUdeerStuf8XLSagX68Miyef16fQNq+o1z8xi988MvWSp3Fe+WHjRjg4YtaVtpjirMu7tCQr0adR2mZ5epxi/hh3R6nI7mlXzZnccuHy6hXK5CvRvd0rIutyMmouHNjQf6+vHJtPLtz8vnr91qeKSJSleZv3MeV7/zK4fxiJt/Zg2u7NnE0z2XxjZh9fx+6Nq/NC9+t59J/LiRp+8GzftzVO7P5ZlUmd/aOoWFYjUpIKtVFh8bHGq20qF+TUROTGTd/C9W590J188O6Pdw+IYmmdYL5clRP/fuQaknFnZvrHB3BnX1i+GzZThZsynI6joiIx7HW8sEvW7ltwnIaRwTzzT29qryZSXlFhdfgoz905d2bunA4v5hr3l3Mw1+tPuPzqqy1vPj9eurWDGB0v9hKTivVQf1aQXwxqieXdmzE32dv4KGvVlNYUup0rGrvm1W7uGvSCto2qsXnI3tQt6Y2iJfqScWdB3hgYEvi6tfk0SlryMnX8kwRkcpSWHKs2+AL361nUNsGTBnds8qappwpYwyD2zdg3kN9Gd03lukrd3HBK/P5dEk6pWUVm5WZm7qXZdsOcv/AltRUJ2aPpUYrFfPZsh3c/8UqujaLYOId3QkPDnA6kshJqbjzAEH+vrw6LJ6so4W88G2q03FERDxC1pFCbnh/KVOSM/7TkKI6bz0THODHY0NaM/v+3rRrFMZT09dy5Tu/snpndrnuX1xaxt9mbSC2XgjDHV5yKlXvvxutXD1ukQq8E/hw4TYe/zqFvi3rMeHWbvrSQ6o9FXceIr5JOKP7xvBVcgb/3rDX6TgiIm7tWFfBhazLzOGtGxJ48MKW+Pi4x3YAcfVDmXxnd94Y3ondOQVc8c6v/HlaCtl5Rae832fLdrB1fy5PXNxGG5Z7kYs7NGTSHT3Ye7iAOz5OIr9ISzTh2BLlN3/czPPfpjKkfQPeuzmRIH/tESjVn969Pci9A1rQKjKUx6amkJOn5ZkiImdiVspurhm3GAtMGX0el3Zs5HSkCjPGMLRTFP9+qC+3ntecz5fv5IJXF/Bl0k7KTrBU83BBMa/P20zPmDpc0Lq+A4nFSV2aRvDG8ARWZ2Rz/xcrK7yc19NYa/nbrA28NncTV3duzD+vTyDATx+ZxT3ob6oHCfTz5dVr4zmQW8SzM9c5HUdExK1Ya3lj3mbumrSC1g1D+eaeXm6/MXFokD9PX9aWmfecT/O6ITwyZQ3Xjl9Maubv9zkbN38LB3OL+PMl2rDcW13UrgFPXdKWOev28uJ33tuBu6zM8tQ3axn/81Zu7tGUl6/pqJlscSv62+ph2keFcXf/OL5euUt72IiIlFN+USn3TF7JP+Zt4qqEKD67swf1Q4OcjlVp2jaqxVejejL2mo5s3Z/LZW8t5LmZqRwpKGZXdj4fLtzGVQlRbl/Mytm57fzm3NqrGf/6dRsf/brN6TjnXElpGQ9PWc3EJTsY3TeW54a2c5vl2CK/0VmhHuie/nHMTd3LE9PW0rVZbSJC1NVJRORkMrPzufOTJFJ3H+bxIa0Z2SfGI2evfHwM1yY2YVDbSF6es5GPFm3j2zWZRNcOxgAPXdTK6YhSDTx5SVsys/N57ttUosJrMKhdA6cjnRNFJWXc9/lKZq3dw8ODWnJ3/ziPfB8Qz6eZOw8U4OfDq8Piyc4r4pkZWp4pInIyK3Yc4vK3fiX9QB4fjkhkVN9Yj/9AFx4cwItXdmD6mF5E1goiKf0Qt53fnKhwbcgs4OtjeP26BOIbh3Pv5ytZVc5uq+6srMwyZtIKZq3dw1OXtuWeC1p4/PuAeC4Vdx6qbaNa3DugBTNWZzIrZbfTcUREqp1ZKbsZ/t4SQgJ9mTbmPC5oHel0pHMqvkk40+/uxeQ7u/PghS2djiPVSI0AXz4YkUj90CDu+Hg5Ow/mOR2pSo3/eSvz1u/lmcvacvv5zZ2OI3JWVNx5sLv6xdI+qhZPTl/LAe1dIyLyHx/9uo0xk1fQvlEtpo3pRYvIUKcjOcLXx3BebF381TBC/kvdmoF8dGtXSsosIz5adtqtNNxVcvohXvlhI5d0aMgfzmvmdByRs6Z3cw/m7+vDq8M6cbigmKe1PFNEhLIyy1+/X8+zM1O5sE0kk+7oQW2dlyxyQrH1avLezYlkHMxn5KfJFJZ41h54OXnF3PvZShqFB/HS1R20FFM8goo7D9eqQSj3D2zJd2t28+2aTKfjiIg4prCklPu+WMV7rhbn427qQo0AbUoscirdmtfmlWvjWbbtIH/6as0J90l0R9Za/jRlNfuOFPDW9Z2pFeTvdCSRSqHizguM6hNDfOMwnpq+lqwjWp4pIt4nJ7+YEf9axszVmTw6uDXPDW2Hr1qci5TL5fGNeGRwK2aszuSVHzY6HadSfLI4nR9S9/Lo4NbENwl3Oo5IpVFx5wX8fH14ZVg8uUWlPDk9BWs941s3EZHyyMzO59p3F5Ocfoh/XBfPXf08vyOmSGW7q28s13eL5p35W5i8dIfTcc7K2l05vPjdei5oXV8NVMTjqLjzEi0iQ3nowpbMWbeXGau1PFNEvMOGPYe56p1F7MrOZ8Kt3bgyobHTkUTckjGG54e2o1+rejz1zVp+2rjP6Uhn5GhhCX/8bCW1QwJ4ZVi8vugRj6Pizovc0TuGhOhwnv5mHfsOFzgdR0SkSi3asp9h4xZjsXw5qie94uo6HUnErfn5+vDWDZ1p3SCUeyatYF1mjtORKsRay1PT15J+IJc3hndSMyXxSCruvIivj+GVYfEUFJfyxDQtzxQRz/XNql2M+NcyGoQF8fWYXrRtVMvpSCIeoWagH//6Q1fCavhz24TlZGbnOx2p3KYkZzBt5S7uG9CS7jF1nI4jUiXKVdwZY7YbY1KMMauMMUmusZeNMRuMMWuMMdOMMeHHHf+4MSbNGLPRGHPRceODXWNpxpjHKv/lyOnE1qvJI4NbM2/9Pr5M2ul0HBGRSmWtZfyCLdz3+SoSoiOYMvo8osJrOB1LxKNE1grio1u7kVdYyq0fLedwQbHTkU4rbd8Rnv5mHT1j6nDPBXFOxxGpMhWZuetvre1krU103Z4LtLfWdgQ2AY8DGGPaAsOBdsBg4B1jjK8xxhd4GxgCtAWudx0r59it5zXjvNg6PDczlR0H8pyOIyJSKUrLLM/OTOWlWRu4pGNDPrmtG2HBam8uUhVaNQjl3Zu7sCXrKGMmrqC4tMzpSCdVUFzKPZNXEhzgy+vDO6lTrni0M16Waa39wVpb4rq5BPjtLPWhwOfW2kJr7TYgDejmuqRZa7daa4uAz13Hyjnm42N4eVg8Psbw4JerKPWQPWtExHsVFJdy96QVTFi0ndvPb84/hycQ5K897ESqUq+4uvzt6o4sTNvPY1NTqu3niee/TWXDniO8em08kbWCnI4jUqXKW9xZ4AdjTLIxZuQJ/vw2YJbrehRw/Hq/DNfYycbFAVHhNXh2aDuS0g/x3s9bnY4jInLGDuUWcdMHS5mTuocnL2nDU5e2xUffzIucE9d0acz9A1swdUUGt05YTnZekdORfue7NbuZtHQHo/rE0K9VfafjiFS58hZ3vay1nTm2pPJuY0yf3/7AGPNnoASY9NvQCe5vTzH+O8aYkcaYJGNMUlZWVjnjyZm4MiGKizs04LW5G0nNPOx0HBGRCtt5MI+r313Emowc3rq+M3f0jnE6kojXuX9gS166qgNLthzgsrcWVpsumjsO5PHY1DUkRIfz8EWtnI4jck6Uq7iz1ma6fu4DpnFsiSXGmBHApcCN9v9bL2YATY67e2Mg8xTj//1c71lrE621ifXq1avYq5EKMcbw4hUdCA8O4IEvVlFQXOp0JBGRctuadZSrxi1i/5FCPr29G5d0bOh0JBGvdX23aL4Y1YPiEsvV4xYxbWWGo3mKSsr442crMAbeHJ6Av68axIt3OO3fdGNMiDEm9LfrwCBgrTFmMPAocLm19viuHDOA4caYQGNMc6AFsAxYDrQwxjQ3xgRwrOnKjMp9OVJRESEBjL2mIxv3HuG1uZucjiMiUi4Zh/K46YOllJVZptx1ntqai1QDCdERzPzj+cQ3DueBL1bzlxnrHGu08vKcDazOyGHsNR1pUjvYkQwiTijP1xiRwEJjzGqOFWnfWWtnA28BocBc1xYJ7wJYa9cBXwKpwGzgbmttqav5yj3AHGA98KXrWHFY/1b1uaF7NO//spUlWw84HUdE5JT2HSngpg+WcqSwhE9u70bLyFCnI4mIS73QQCbe0Z3bz2/OhEXbufH9pew7UnBOM/x7w17e/2UbN/doyuD2mtEX72Kq80bWiYmJNikpyekYXiG3sIRL3vyF4lLL7Pt7Exqk9uEiUv0cyi1i+HtL2Hkoj09v706XphFORxKRk/hm1S4enbqGsBr+vHNjl3Py73VPTgFD3viZBmE1mDbmPHXNFY9gjEk+bju6U9ICZAEgJNCP167rxO6cfJ6dmep0HBGR/3GkoJg/fLSMbQdyef+WRBV2ItXc0E5RTBvTi0A/X4a/t5iJS9KpykmF0jLLfZ+vpLCkjLdu0HYo4p1U3Ml/dI6OYEy/OKYkZzBn3R6n44iI/Ed+USm3f5zE2szDvHNDZ3rF1XU6koiUQ5uGtZh5z/n0iqvLk9PX8siUNVXWwO3NHzezdNtBnh/anth6NavkOUSqOxV38jv3DmhBu0a1ePzrFLKOFDodR0SEopIy7pqUzPLtB3nt2ngGto10OpKIVEBYsD8fjujKvRfE8VVyBteOX8yu7PxKfY7FWw7wz39v5qrOUVzdpXGlPraIO1FxJ78T4OfD69d14mhhCY9NXVOlyydERE6npLSM+z5fyfyNWbx0ZQeGdopyOpKInAFfH8ODg1rx/i2JbMvK5bJ/LmRR2v4zfrxDuUX8vCmLt39KY/SnyYz8NIlmdUN4fmj7Skwt4n7UUEVO6MOF23j+21T+dlUHhneLdjqOiHihsjLLI1PXMCU5gycvaaMNykU8xJaso4z+NJktWUd5bEhr7uwdgzHmpMcfyi0iZVfOsUvGsZ/Hz/w1rRNMh6gwHriwpZZjikeqSEMVFXdyQmVllps+XMqqndnMuq83TeuEOB1JRLyItZZnZ6YyYdF27h/YgvsHtnQ6kohUoqOFJTwyZTXfp+zhko4NGXt1R0IC/U5byDWrE0z7qDA6uC7tosIIq6EO3+LZVNxJpdiVnc/g13+mVWQoX4zqia/Pyb9VExGpTC/P2cDbP23hjvOb8+dL2pzyW30RcU/WWsb/vJWxszfQKLwG1qJCTuQEKlLc+VV1GHFfUeE1eG5oOx74YjXjf97CmH5xTkcSES/wzvw03v5pC9d3a6LCTsSDGWMY3TeW9o3CeOPHTUTWCuKWnk3p0DiMdo1UyImcCRV3ckpXdIpibupe/jF3E31b1qNdozCnI4mIB/t08XbGzt7I5fGNeOGKDirsRLzA+S3qcn4LbW8iUhnULVNOyRjDi1d0IDw4gAe+WFVle9OIiExNzuCpb9YxsE0kr14br6XgIiIiFaTiTk4rIiSAsdd0ZNPeo7z6w0an44iIB5q9djd/mrKaXnF1eOuGBPx99b8nERGRitL/PaVc+reqz43do/lg4TYWbzngdBwR8SALNmXxx89W0qlJOO/dnEiQv6/TkURERNySijsptz9f0oamtYN5+KvVHC4odjqOiHiAZdsOMurTJFrUD+WjW7sREqhTwUVERM6Uijspt+AAP167rhO7c/J5dkaq03FExM2l7TvKbROWExVeg09v76bOeCIiImdJxZ1USOfoCO7uH8fUFRlMX7nL6Tgi4qastTwxLQVfH8PEO7pTp2ag05FERETcnoo7qbD7BrSga7MInpiWwpaso07HERE39FVyBsu2HeTxIa1pGFbD6TgiIiIeQcWdVJifrw9vXp9AoJ8Pd09aoe0RRKRCDuYW8dL360lsGsG1iU2cjiMiIuIxVNzJGWkYVoPXruvEhj1HeHamzr8TkfJ76fv1HCko4cUrO+CjvexEREQqjYo7OWP9W9VndN9YPlu2g29W6fw7ETm9pVsP8FVyBnf0jqFVg1Cn44iIiHgUFXdyVh4a1JLEphE88XUKW3X+nYicQlFJGX+evpbGETW4b0ALp+OIiIh4HBV3clb8XeffBfj5cPfklTr/TkRO6r2ft5C27yjPD21PjQBtVC4iIlLZVNzJWWsUXoPXru3E+t2Hee5bnX8nIv8r/UAu//x3Ghd3aED/1vWdjiMiIuKRVNxJpejfuj6j+sYweekOZqzOdDqOiFQj1lqe+mYd/r4+PH1pO6fjiIiIeCwVd1JpHh7Uii5NI3h86hq27c91Oo6IVBPfrtnNz5uyeGhQSxqEBTkdR0RExGOpuJNK89v5d/5+PozR/nciAuTkF/Pct6l0iArjlp7NnI4jIiLi0VTcSaWKCq/Bq8PiWb/7MC98p/PvRLzdqz9s5MDRQv56ZQd8taediIhIlVJxJ5VuQJtIRvaJYeKSHczU+XciXmvVzmw+XZLOLT2b0aFxmNNxREREPJ6KO6kSf7qoFQnR4Tz+dQrbdf6diNcpKS3jia9TqB8ayEODWjodR0RExCuouJMq4e/rw1s3dMbXx3D3ZJ1/J+JtJizaTuruwzxzWTtCg/ydjiMiIuIVylXcGWO2G2NSjDGrjDFJrrHaxpi5xpjNrp8RrnFjjHnTGJNmjFljjOl83OOMcB2/2RgzompeklQXv51/ty7zMC9+t97pOCJyjmRm5/Pa3E30b1WPIe0bOB1HRETEa1Rk5q6/tbaTtTbRdfsx4EdrbQvgR9dtgCFAC9dlJDAOjhWDwDNAd6Ab8MxvBaF4roFtI7mzd3M+XZLOt2t0/p2IN3h25jrKrOW5oe0xRk1UREREzpWzWZY5FPjYdf1j4Irjxj+xxywBwo0xDYGLgLnW2oPW2kPAXGDwWTy/uIlHBrcmITqcx6bq/DsRTzcvdS9z1u3lvgEtaVI72Ok4IiIiXqW8xZ0FfjDGJBtjRrrGIq21uwFcP+u7xqOAncfdN8M1drLx3zHGjDTGJBljkrKyssr/SqTa8vf14Z/XJ+DrY7jnsxUUluj8OxFPlFdUwjMz1tEysiZ39G7udBwRERGvU97irpe1tjPHllzebYzpc4pjT7QGx55i/PcD1r5nrU201ibWq1evnPGkumscEcwrw+JZu+swf9X5dyIe6fV5m9mVnc9fr+yAv6/6dYmIiJxr5fq/r7U20/VzHzCNY+fM7XUtt8T1c5/r8AygyXF3bwxknmJcvMSFbSO5/fzmfLw4ne9TdjsdR0Qq0frdh/lw4TaGd21CYrPaTscRERHxSqct7owxIcaY0N+uA4OAtcAM4LeOlyOAb1zXZwC3uLpm9gByXMs25wCDjDERrkYqg1xj4kUeHdya+CbhPDpljc6/E/EQZWWWJ6alEF7Dn8eGtHY6joiIiNcqz8xdJLDQGLMaWAZ8Z62dDfwNuNAYsxm40HUb4HtgK5AGvA+MAbDWHgSeB5a7Ls+5xsSLBPj58Nb1Cfj6GkZPTCa/SOffibi7z5bvYOWObP58SRvCgwOcjiMiIuK1jLX/c9pbtZGYmGiTkpKcjiFVYMGmLP7w0TKGxjfiH9d1Urt0ETeVdaSQAa/Op12jMCbf2V3/lkVERCqZMSb5uO3oTklnvIsj+rasx4MDWzJ9VSafLE53Oo6InKEXvkuloLiMF67UnnYiIiJOU3Enjrm7fxwDWtfn+W9TSU7XCl0Rd7Noy36+WZXJ6H6xxNar6XQcERERr6fiThzj42N47bpOREXU4K6JK9h3pMDpSCJSAe/9vJX6oYGM6RfrdBQRERFBxZ04LKyGP+/e1IXDBcXcM3klxaVlTkcSkXLYeTCPBZuyGN4tmiB/X6fjiIiICCrupBpo07AWL13VgWXbDvL3WRucjiMi5fD58h0YYHjXJqc9VkRERM4NFXdSLVyZ0JgRPZvywcJtfLtGe9uLVGfFpWV8sTyDC1rXp1F4DafjiIiIiIuKO6k2/nxJW7o0jeCRKWvYtPeI03FE5CTmpu5l/9FCbuze1OkoIiIichwVd1JtBPj58M6NnQkO8GP0p8kcKSh2OpKInMCkpelEhdegT8t6TkcRERGR46i4k2olslYQb92QQPrBPB7+ajXWWqcjichxtu3P5de0A1zfrQm+PtrXTkREpDpRcSfVTo+YOjw+pDVz1u3l3QVbnY4jIsf5bNkO/HwM1yaqkYqIiEh1o+JOqqXbz2/OJR0b8vKcDfyatt/pOCICFBSX8lXSTi5sG0n9WkFOxxEREZH/ouJOqiVjDGOv7khMvZr88bOVZGbnOx1JxOvNWbeHQ3nF3NA92ukoIiIicgIq7qTaCgn0492bulBUUsZdk1ZQWFLqdCQRrzZpyQ6a1gmmV2xdp6OIiIjICai4k2otrn5NXhnWkdU7s3l2ZqrTcUS81ua9R1i2/SA3dIvGR41UREREqiUVd1LtDW7fkNF9Y5m8dAdfJu10Oo6IV5q0dAcBvj5c06Wx01FERETkJFTciVt4eFBLzoutw5PT17J2V47TcUS8Sn5RKV+vyGBw+wbUqRnodBwRERE5CRV34hb8fH148/oE6oQEMHpiModyi5yOJOI1vl2TyeGCEjVSERERqeZU3InbqFszkHE3dWHf4ULu+2IVpWXa4FzkXJi8bAex9ULo3ry201FERETkFFTciVvp1CScZy5vy8+bsnh93ian44h4vHWZOazckc0N3ZtijBqpiIiIVGcq7sTt3NAtmmFdGvPPf6fxw7o9TscR8WiTl+4g0M+HqztHOR1FRERETkPFnbgdYwzPX9Gejo3DePDL1WzJOup0JBGPdLSwhOkrd3Fpx0aEBwc4HUdEREROQ8WduKUgf1/G3dSFAD8fRn2azNHCEqcjiXicGasyyS0qVSMVERERN6HiTtxWVHgN3ro+ga1ZR3n4y9VYqwYrIpXFWsukpem0bhBK5+hwp+OIiIhIOai4E7d2XlxdHh/Shtnr9jBuwRan44h4jDUZOazLPMyN3aPVSEVERMRNqLgTt3dH7+ZcFt+IV+Zs5OdNWU7HEfEIk5fuIDjAlysS1EhFRETEXai4E7dnjOHvV3egZWQo936+kp0H85yOJOLWDhcUM2N1JpfHNyI0yN/pOCIiIlJOKu7EIwQH+DH+5i6UlVlGfZpMflGp05FE3Nb0lbvILy7lxu5NnY4iIiIiFaDiTjxG0zohvDE8gfV7DvPEtBQ1WBE5A9ZaJi3ZQYeoMDo0DnM6joiIiFSAijvxKP1b1+eBgS2ZtnIXExZtdzqOiNtJTj/Exr1HuFHbH4iIiLidchd3xhhfY8xKY8y3rtsDjDErjDGrjDELjTFxrvFAY8wXxpg0Y8xSY0yz4x7jcdf4RmPMRZX9YkQA7ukfx8A2kbz43XqWbj3gdBwRtzJ56Q5CA/24LL6R01FERESkgioyc3cfsP642+OAG621nYDJwJP/196dx0dV3f8ff32yhyVhl5AEQdkRFIwsaq1bBZdqrbVCXVBx19bavd/+unztoy5dbK3fWhdwX8C12lZA626VJVBZg8gmBAg7IYGQ9fP7Yy40xiQESLiTmffz8ZgHN2fOufncOcxMPvece25QPhHY7u59gD8CdwOY2SBgHDAYGAvcb2aJhxa+yBclJBj3XHIsPTu14eZn5lFUvCfskERahe27KvjHwg18bVg2bVOTwg5HREREDlCTkjszywHOBSbVKnYgI9jOBNYH2xcAjwfbLwBnWOQmSRcAU9y93N1XAcuBEYcWvkj9MtKSefDy4ymrqOaGp+ZSXqUFVkT258V5hVRU1fAtTckUERFplZo6cvcn4EdATa2ya4DXzKwQuBy4KyjPBtYCuHsVUAx0rl0eKPzZ3kIAACAASURBVAzKPsfMrjOzfDPL37xZ9yyTg9f3iPb8/uJj+XjtDv7370vCDkckqrk7z8xew/CeHRiYlbH/BiIiIhJ19pvcmdl5wCZ3n1vnqduAc9w9B3gUuGdvk3p2442Uf77A/SF3z3P3vK5du+4vPJFGnT0kixtPPZpnZq1h6pw1YYcjErVmrtzGys27dPsDERGRVqwpF1WcBJxvZucAaUCGmf0TGODus4I6U4HpwXYhkAsUmlkSkSmb22qV75XDf6dyirSYH5zVn0Xrivn53xbTv3sGx+V2CDskkajz9KzPyExP5tyhWWGHIiIiIgdpvyN37v5Td89x915EFkR5i8j1c5lm1i+o9hX+u9jKq8CEYPsbwFseueHYq8C4YDXN3kBfYHazHYlIAxITjD+PG0a3jFRufGouW0rLww5JJKpsKS1nxuIiLhqeQ1qy1rkSERFprQ7qPnfBtXTXAi+a2Xwi19z9MHh6MtDZzJYD3wN+ErRZDDwHLCEyynezu2uVCzksOrZN4YHLjmfbrgpufnoeVdU1+28kEieezy+kstq1kIqIiEgrZ5FBteiUl5fn+fn5YYchMeTl/xRy29T5TDy5Nz8/b1DY4YiErqbGOfX375CVmcbU60eHHY6IiIjUYWZz3T2vKXUPauROpLW6cFgOV57Yi8kfrOKleYVhhyMSug+Wb2HNtt1cOkoLqYiIiLR2ukutxJ2fnTuQT4pK+NELC+jYJoXTBnQLOySR0Dw96zM6tU1hzOAjwg5FREREDpFG7iTuJCcm8NAVxzMwK4MbnprL7FXbwg5JJBQbd+7hXwWbuDgvh9QkLaQiIiLS2im5k7jUPi2Zx646gZyO6Ux8bA6L1hWHHZLIYffcnLVU1zjjT9BCKiIiIrFAyZ3Erc7tUnly4kjapyUx4ZHZrNxcGnZIIofVK/PXM+qoTvTq0jbsUERERKQZKLmTuNajQzpPXjMSgMsnz2ZDcVnIEYkcHss3lbB8UynnDNFNy0VERGKFkjuJe0d3bcfjV49gZ1kll02axbZdFWGHJNLiZizeCMBZg7qHHImIiIg0FyV3IsAx2ZlMmpBH4fYyrnx0NqXlVWGHJNKipi3awLCeHeiemRZ2KCIiItJMlNyJBEYe1Zm/XjacJet3cu3j+eyprA47JJEWsXbbbhat28nZx2jUTkREJJYouROp5fQBR/CHbx7LzFVb+faz/6GquibskESa3YzFRQCMHazr7URERGKJkjuROi44Lpv/PX8wbyzZyI9fXEhNjYcdkkizmr6oiEFZGfTs3CbsUERERKQZJYUdgEg0umJ0L3bsruSeN5aRkZ7EL84bhJmFHZbIIdu0cw9z12zntjP7hR2KiIiINDMldyIN+Pbpfdixu5JH/r2Kjm1S+M4ZfcMOSeSQzViyEXcYq+vtREREYo6SO5EGmBn/79yBFJdFRvAy05OZcGKvsMMSOSQzFhVxVNe29O3WLuxQREREpJkpuRNpREKCcfdFQ9i5p5JfvrqYzPRkvjYsO+ywRA7K9l0VfLRyK9efcpSmGYuIiMQgLagish9JiQncN34Yo4/qzPefn8+bBRvDDknkoPyrYCPVNc7Zx2iVTBERkVik5E6kCdKSE3l4Qh6De2Rw09PzmLVya9ghiRyw6YuKyO6QzjHZGWGHIiIiIi1AyZ1IE7VLTeKxq0aQ0zGdax7PZ9G64rBDEmmy0vIq3v90C2MGd9eUTBERkRil5E7kAHRqm8JT14wkIz2ZCY/MZsXm0rBDEmmSt5duoqK6hrOHaJVMERGRWKXkTuQAZWWm89Q1IzGDyyfNYt2OsrBDEtmv6YuK6NIuleE9O4YdioiIiLQQJXciB6F3l7Y8cfVISsqruHzSLLaUlocdkkiD9lRW8/Ynmxgz+AgSEzQlU0REJFYpuRM5SIN6ZPDolSewvriMKybPprisMuyQROr13rLN7K6o1o3LRUREYpySO5FDkNerEw9ensenm0q45vE5lFVUhx2SyBdMX1xEZnoyo47qHHYoIiIi0oKU3Ikcoi/368qfLhnG3M+2c8NTc6moqgk7JJF9Kqtr+NeSjZw58AiSE/WRLyIiEsv0TS/SDM4dmsUdFw7h3WWbue25j6mu8bBDEgHgoxVb2bmnSlMyRURE4kBS2AGIxIpxI3qyc08ld7y2lIy0JO64cIjuJyahm764iDYpiXypb5ewQxEREZEWpuROpBldd8rRFJdV8pe3V5CRnsxPzx4YdkgSx6prnNcXF3HagG6kJSeGHY6IiIi0MCV3Is3sB2f1Z2dZFQ++u5LM9GRuOrVP2CFJnJr72Xa2lFYwdrCmZIqIiMQDJXcizczM+N/zB7NzTyW/nf4JGWnJXDbqyLDDkjg0fVERKUkJnDagW9ihiIiIyGHQ5AVVzCzRzP5jZv8IfjYz+42ZLTOzAjP7Tq3yP5vZcjNbYGbDa+1jgpl9GjwmNP/hiESHhATj9xcfyxkDuvHzVxbx6vz1YYckccbdmbG4iFP6dqFdqs7jiYiIxIMDWS3zVqCg1s9XArnAAHcfCEwJys8G+gaP64C/AphZJ+CXwEhgBPBLM+t4KMGLRLPkxAT+culwRvTqxPemfszbSzeFHZLEkYXrilm3o4yxx2SFHYqIiIgcJk1K7swsBzgXmFSr+EbgdnevAXD3vX+5XgA84REzgQ5mlgWMAd5w923uvh14AxjbTMchEpXSkhOZNCGPgVkZ3PDUXGav2hZ2SBInpi0qIjHBOHOgpmSKiIjEi6aO3P0J+BFQ++7MRwOXmFm+mU0zs75BeTawtla9wqCsofLPMbPrgn3mb968uYnhiUSv9mnJPHbVCeR0TGfiY3NYtK447JAkxrk70xcVMfqoznRokxJ2OCIiInKY7De5M7PzgE3uPrfOU6nAHnfPAx4GHtnbpJ7deCPlny9wf8jd89w9r2vXrvsLT6RV6NwulScnjiQjPZkJj8xmxebSsEOSGPbpplJWbdmlG5eLiIjEmaaM3J0EnG9mq4lcV3e6mT1FZOTtxaDOy8DQYLuQyLV4e+UA6xspF4kLPTqk89Q1IzGDyyfNYt2OsrBDkhg1bWERZnDW4CPCDkVEREQOo/0md+7+U3fPcfdewDjgLXe/DPgbcHpQ7cvAsmD7VeCKYNXMUUCxu28AZgBnmVnHYCGVs4IykbjRu0tbnrh6JCXlVVz68EzWK8GTFjB9cRF5R3akW/u0sEMRERGRw+hAVsus6y7gIjNbCNwJXBOUvwasBJYTma55E4C7bwN+DcwJHrcHZSJxZVCPDB67agRbSyv45oMfsWbr7rBDkhjy2dZdFGzYyRjduFxERCTumPsXLnuLGnl5eZ6fnx92GCItYmFhMZc/MovUpASevmYUfbq1CzskiQEPvruCO6ct5f0fnUZupzZhhyMiIiKHyMzmBuuc7NehjNyJyCEYkpPJlOtGUV0Dlzz4EQUbdoYdksSAaYuKGJKdqcROREQkDim5EwnRgO4ZTL1+FMmJCYx7aCbz1+4IOyRpxTYUl/Hx2h1aJVNERCROKbkTCdnRXdvx/A2jyUhP4tJJs5izWpeiysF5ffFGACV3IiIicUrJnUgUyO3UhueuH0239qlcMXk2H3y6JeyQpBWatmgDfbu14+iuun5TREQkHim5E4kSWZnpTL1+NEd2bsPVj8/hraUbww5JWpGtpeXMXrVNo3YiIiJxTMmdSBTp2j6VZ68dxYDu7bnuibm8tnBD2CFJK/HGko3UuKZkioiIxDMldyJRpmPbFJ66ZiTH5Xbglmfm8dK8wrBDklZg+uIicjulMygrI+xQREREJCRK7kSiUEZaMk9MHMGoozrz/efn88ysNWGHJFFs555K/r18C2cfk4WZhR2OiIiIhETJnUiUapOSxCNXnsCp/bryPy8vZPIHq8IOSaLUWwWbqKx2xgzWlEwREZF4puROJIqlJSfy4OV5nH1Md379jyX831ufhh2SRKHpi4o4IiOVYbkdwg5FREREQqTkTiTKpSQlcN/4YVw4LJvfv76M305firuHHZZEid0VVbyzbBNjBncnIUFTMkVEROJZUtgBiMj+JSUm8IeLjyUtOYH731nB7opqfvnVQbq+Snhv2Wb2VNYwVlMyRURE4p6SO5FWIiHBuOPCIaQlJ/Lov1dTVlHNL746iLapehvHs+mLiujYJpkRvTuFHYqIiIiETH8VirQiZsYvzhtEm5RE/vL2CmYsKeLKE3tx5Ym96NAmJezw5DArr6rmzYJNnD2kO0mJmmUvIiIS7/TXgEgrY2b8cMwAXrrpRPKO7MSf/vUpJ931Fne+VsCmkj1hhyeH0YcrtlJSXqUbl4uIiAigkTuRVmt4z45MmpBHwYad/PWdFTz8/koe/XA1l+Tlct0pR5HbqU3YIUoLm76wiHapSZzUp0vYoYiIiEgU0MidSCs3MCuDP48fxlvfP5WLhmczZc4aTv39O3zvuY9Zvqkk7PCkhTyXv5bn565l7DHdSU1KDDscERERiQIWzUuq5+XleX5+fthhiLQqG4rLePi9VTw7ew17qqoZO7g7N53ahyE5mWGHJs3kgXdXcNe0pXypbxceuOx4LaojIiISw8xsrrvnNamukjuR2LS1tJzHPlzNYx+upmRPFaf068otp/XRqoqtmLtz57SlPPTeSs4bmsU93zyOlCRNwBAREYllSu5EZJ+SPZU8OfMzJr+/iq27KjihV0duOq0Pp/brqvvktSJV1TX8+MWFvDivkCtGH8mvvjpYNy0XERGJA0ruROQLyiqqeS5/LQ++u4L1xXsY3CODuy8ayjHZmq4Z7fZUVnPz0/N4c+kmvntmX249o68ScxERkThxIMmd5vOIxIn0lEQmnNiLd354Gr/7xlC2llZw8QMfMWNxUdihSSOKyyq5fPIs3vpkE7++YDDfPbOfEjsRERGpl5I7kTiTkpTAxXm5vPrtk+jXvT03PDWXB95dQTSP4serTTv3cMmDH/Hx2h3cN34Yl4/uFXZIIiIiEsWU3InEqW7t05h63SjOHZLFXdOW8sMXFlBRVRN2WBJYvWUXFz3wIWu27eaRK0/gvKE9wg5JREREopzWzxaJY2nJidw3fhhHd23HvW9+ypptu3ngsuPp1DYl7NDi2qJ1xVz56Byqa2p49tpRHJvbIeyQREREpBXQyJ1InDMzbvtKP+4ddxwfr93B1/7yb938PEQzV25l/EMzSUk0nr/hRCV2IiIi0mRK7kQEgAuOy2bKdaPYXVHNhfd/yHvLNocdUtyZsbiIKx6ZzRGZabx404n06dYu7JBERESkFVFyJyL7DO/ZkVduOYnsDulc9dgcnvxoddghxY2pc9Zw41NzGZSVwfPXjyYrMz3skERERKSVaXJyZ2aJZvYfM/tHnfL7zKy01s+pZjbVzJab2Swz61XruZ8G5Z+Y2ZjmOAARaV7ZHdJ54cYTObVfV37+ymJ+9epiqqq10EpLcXf++s4KfvziQk7u25Vnrh1JR13zKCIiIgfhQEbubgUKaheYWR5Q94KQicB2d+8D/BG4O6g7CBgHDAbGAvebWeJBxi0iLahdahIPXZHHtV/qzWMfrubqx/PZuacy7LBiTk2N85t/FnD39KWcf2wPJl2RR5sUrXMlIiIiB6dJyZ2Z5QDnApNqlSUCvwN+VKf6BcDjwfYLwBkWuePuBcAUdy9391XAcmDEoYUvIi0lMcH42bmDuOvrQ/hw+RYuuv9D1m7bHXZYMWPH7gq+99zHTPpgFVee2Is/XXIcKUmaKS8iIiIHr6l/SfyJSBJXe27WLcCr7r6hTt1sYC2Au1cBxUDn2uWBwqBMRKLYuBE9eWLiCDaVlHPBX/7NnNXbwg6pVSuvqubh91Zyym/f5pX56/n+V/rxy68OIiHBwg5NREREWrn9Jndmdh6wyd3n1irrAVwM3Fdfk3rKvJHyur/vOjPLN7P8zZu1Wp9INDjx6C787eaT6JCezKUPz+LFuYVhh9TquDuvzl/PGX94l9+8VsCwnh2ZduuX+PYZfYlMbhARERE5NE25uOMk4HwzOwdIAzKAxUA5sDz4o6SNmS0PrrMrBHKBQjNLAjKBbbXK98oB1tf9Ze7+EPAQQF5e3heSPxEJR+8ubXn5ppO48em5fP/5+azYXMoPzuqvEacmmLVyK3e8VsD8wmIGZmXw5MQhfKlv17DDEhERkRhj7k3Pn8zsVOAH7n5enfJSd28XbN8MDHH3G8xsHPB1d/+mmQ0GniFynV0P4E2gr7tXN/T78vLyPD8//0CPSURaUGV1Db94ZTHPzl5DZnoyQ7IzGZKTydDsTIbmdqBHZppGogIrNpdy17SlvLFkI90z0vjBmP5cOCybRCXEIiIi0kRmNtfd85pStyWWZZsMPGlmy4mM2I0DcPfFZvYcsASoAm5uLLETkeiUnJjAHRcewyl9u/D+8i0sKNzBw++tpKomcqKoc9uUfcnekJwODM3J5IiMtJCjPry2lJZz778+5ZnZa0hPTuSHY/pz9Um9SU/RAsEiIiLScg5o5O5w08idSOuwp7KaT4pKWLCumIWFO1hQWMyyjSUE+R7d2qcyNCeToTkdGJKTyZDsTLq0Sw036BZQVlHN5A9W8sC7KymrrOZbI3py65l9Y/JYRURE5PAIe+ROROJMWnIix+Z24NjcDsCRQCTRWbKhmAWFxSwsLGbBumLeXLqJveeTsjukc3KfLlwyIpdhuR1a9VTO6hrnpXmF/OH1ZRTt3MNXBh3BT84ewNFd24UdmoiIiMQRJXci0iLSUxI5/shOHH9kp31lpeVVLF4XSfg+LtzB3xesZ2r+Wvof0Z5xI3L5+rAcMtskhxj1gXv/08385p8FLC0q4dicTO4ddxwjj+ocdlgiIiIShzQtU0RCU1pexd/nr+fZ2WtYUFhMSlIC5xzTnXEjejKyd6eoG81zdzaXlLO0qISlRTt5b9kWPli+hZyO6fxo7ADOG5Kl1UNFRESkWR3ItEwldyISFRavL2bK7LX87eN1lOyp4qiubRl3Qi4XDc+hcwjXrO2prGbZxhKWbijZl8wtLSph266KfXWyMtOYeHJvLh99JKlJWixFREREmp+SOxFptcoqqvnnwg1Mmb2G/M+2k5xonDWoO+NG5HLS0V2afWSspsZZt6OMgg2R5O2TohIKinayesuufQvCpCUn0P+I9gzonsGArODf7u3p2DalWWMRERERqUvJnYjEhE83ljBlzlpemlfI9t2V5HZK55K8XC7Oy93v7RVqapwdZZVsLS1n664Ktu2q2Le9tTTy84biMpZtLKW0vGpfuyM7t4kkclkZDOwe+bdnpza6N52IiIiEQsmdiMSU8qpqZizeyJTZa/hwxVYSE4zT+nfj5D6dKS6rYuuuvUlbOduCRG7brop9I291dWiTTKe2KXRrn0q/WiNy/Y5oT7tUrTMlIiIi0UPJnYjErNVbdjE1fy3P5xeypbQcgMz0ZDq3TaFzuxQ6tU2hc7tUOretbzuFjm1SSE5MCPkoRERERJpGyZ2IxLzK6hq2765QsiYiIiIxTTcxF5GYl5yYQLf2jV93JyIiIhJPdLpbREREREQkBii5ExERERERiQFK7kRERERERGKAkjsREREREZEYoOROREREREQkBii5ExERERERiQFK7kRERERERGKAkjsREREREZEYoOROREREREQkBii5ExERERERiQFK7kRERERERGKAkjsREREREZEYYO4edgwNMrMS4JOw46hHT2BN2EEImUBx2EHEOfVB+NQH4VMfhE99ED71QfjUB9GhJfKE/u7evikVk5r5Fze3T9w9L+wg6jKzzdEYV7wxs4fc/bqw44hn6oPwqQ/Cpz4In/ogfOqD8KkPokNL5Almlt/UupqWeXB2hB2AAPD3sAMQ9UEUUB+ET30QPvVB+NQH4VMfRIdQ84Ron5aZH40jZNEal4iIiIiIhKcl8oQD2We0j9w9FHYADYjWuEREREREJDwtkSc0eZ9RPXInIiIiIiIiTRPtI3cSJ8xsrJl9YmbLzewnQdlkM5tvZgvM7AUza9dA258G7T4xszGN7VMa1kAfmJn9xsyWmVmBmX2ngbYTzOzT4DGhVvnxZrYw2OefzcwO1/G0Rg30welmNs/MFpnZ42ZW70JY6oPmYWaPmNkmM1tUq+x3ZrY0+Cx62cw6NNC23s8cM+ttZrOCvplqZimH41haqwb64Fdmts7MPg4e5zTQVn3QDBrog+PMbGbw+ueb2YgG2uqzqBmYWa6ZvR189y42s1uD8ouDn2vMrMFpenovxDF310OPUB9AIrACOApIAeYDg4CMWnXuAX5ST9tBQf1UoHewn8SG9hn2sUbro5E+uAp4AkgI6nWrp20nYGXwb8dgu2Pw3GxgNGDANODssI81Wh+N9MFaoF9Q53ZgovqgRfvhFGA4sKhW2VlAUrB9N3B3U/sveO45YFyw/QBwY9jHGc2PBvrgV8AP9tNOfdCyffD63s8P4BzgnXra6bOo+fogCxgebLcHlgXfCQOB/sA7QF4DbfVeiOOHRu5o8Gx5k85smEaNmsMIYLm7r3T3CmAKcIG774TI6BGQDtQ3h/gCYIq7l7v7KmB5sL9693kYjqW1auj1uhG43d1rANx9Uz1txwBvuPs2d98OvAGMNbMsIgn6R+7uRJLErx2Og2ml6uuDi4Byd18W1HkjKKtLfdBM3P09YFudstfdvSr4cSaQU0/Tet9DwefX6cALQb3HUR80qr4+aCL1QTNpoA8cyAi2M4H19TTVZ1EzcfcN7j4v2C4BCoBsdy9w9/3dA1rvhWbSGnOEuE/uzCwR+AtwNpEzIuPNbBCRs7N/dPe+wHZgYj1tBwHjgMHAWOB+M0tsZJ9Sv2wioxN7FQZlmNmjQBEwALgvKDvfzG7fT9sG9yn1auj1Ohq4JJiCM83M+gKYWZ6ZTdpP2+xgu2651K++17E7kFxr6s03gFxQH4ToaiKjDphZDzN7LShvqA86AztqJYfqg4N3i0Wmxj5iZh1BfXCYfRf4nZmtBX4P/BT0WXQ4mFkvYBgwq5E6ei80s9aaI8R9ckfDIxZNObOhUaPmUd+8ewdw96uAHkTOWF0SlL3q7r/YT9sG9yn1auj1SgX2eGT53YeBRwDcPd/dr9lPW/XBganv9aoh8uXwRzObDZQAVaA+CIOZ/YzI6/80gLuvd/e9136pD1rWX4mcbDoO2AD8AdQHh9mNwG3ungvcBkwGfRa1NIusN/Ai8N29M5rqo/dCi2iVOYKSu4bPbtR7ZkOjRi2ikGA0IpBDreke7l4NTKX+6WgNtW10n/IFjb2OLwZlLwNDD7BtTj3lUr96X8dgGtOX3H0E8B7waVPboj5oNsHCEOcBlwbTyupqqA+2AB3svwvhqA8OgrtvdPfqYIr4w0T+QKpLfdCyJgAvBdvPc2B9oM+ig2BmyUS+g59295f2V78WvReaR6vMEZTc1X8WI7Gesr0jSRo1an5zgL7BHOYUIiMVr5pZH9h3zd1XgaX1tH0VGGdmqWbWG+hL5KLtevd5GI6ltWro9fobkTNUAF8mckF3XTOAs8ysYzBV6ixghrtvAErMbFTQh1cAr7T0gbRiDb0PugGYWSrwYyIXwNelPmhBZjaWyGt/vrvvbqBavf0XJIJvE5lSC5E/kNUHByi4ZmuvC4FF9VRTH7Ss9US+ByDyvVDfiSZ9FjWT4HWaDBS4+z0H2FzvhebRKnMEJXf1n91YQ9PObGjUqBkEZz9uIfKlUEBkJacC4HEzWwgsJLJq1O3w+TMj7r44qL8EmA7cHJzd/cI+g7pSj0Zer7uAi4J+uBO4Bj5/jYW7bwN+TeTLZA6RBVj2Xoh/IzCJyHSEFQTXKskXNdIHPzSzAmAB8Hd3fwvUBy3FzJ4FPgL6m1mhmU0E/o/IanVvWGQZ+AeCuvuucdnPZ86Pge+Z2XIi17xMPqwH1co00Ae/tcgy+guA04hMC1QftJAG+uBa4A9mNh+4A7guqKvPopZxEnA5cLrVugWImV1oZoVEVh79p5nNAL0XWkirzBHi/ibmQecsA84A1hH5MPoWkWWXX3T3KcEX+QJ3v79O28HAM0SmJvQA3iQycmT17VPJhYiIiIhI9GutOULcj9w1cnaj3jMbGjUSEREREYltrTVHiPuROxERERERkVgQ9yN3IiIiIiIisUDJnYiIiIiISAyIu+TOzHLN7G0zKzCzxWZ2a1D+OzNbamYLzOxlM+vQyD6SzGyLmd15+CIXEREREZGW0EiO8OsgP/jYzF43sx6N7KOrmVWa2fWHL/I6McTbNXfBvXKy3H2embUH5hK5s3wO8Ja7V5nZ3QDu/uMG9nEO8DOgO9CngRvaioiIiIhIK9BIjlDo7juDOt8BBrn7DQ3s4yZgPFDt7qcensg/L+5G7tx9g7vPC7ZLiKxUk+3ur9e62/xMIsleQ8YD9xK518WovYVmttrMugTbeWb2TrDd1czeMLN5ZvagmX22t56IiIiIiISrkRxhZ61qbWn8puPjge8DOWaWvbfQzEprbX/DzB4Lto82s5lmNsfMbq9d72DFXXJXm5n1AoYBs+o8dTUN3FzTzNKJ3JviH8CzRDpxf35JZFRwOPAy0PPgIhYRERERkZZUN0cws9+Y2VrgUuAXDbTJBbq7+2wit0G4pAm/6l7gXnc/gWa6mXncJndm1g54Efhu7YzczH4GVAFPN9D0POBtd98dtL/QzBL38+tOBqYAuPt0YPshhi8iIiIiIs2svhzB3X/m7rlE8oNbGmg6jkhSB5G/+5syADQaeD7Yfuagg64lLpM7M0sm0mlPu/tLtconEEneLt17HZ2ZPRpcQPlaUG08cKaZrSYyF7czcFrwXBX/fU3Tav/KljoWERERERE5dA3lCLU8A1wU1J0R5AiTgufGA1cGOcKrwLFm1jd4rvZUzto5QrOLu+TOzIzIneQL3P2eWuVjidxx/vxgVA4ADCMTCAAAAY9JREFUd7/K3Y9z93PMLIPIKFxPd+/l7r2Am/lvZr4aOD7YvqjWr/0A+Gbwe84COrbEsYmIiIiIyIFrJEfoW6va+cBSAHcfE+QI15hZf6Ctu2fXyhHuJDKaB7DRzAaaWQJwYa39zeS/OcM4mkE8rpZ5MvA+sBCoCYr/B/gzkApsDcpm1l0Jx8yuBMa6+7haZZ2AT4gswDKCyH+KjUTm6Oa5+6lm1o3I9XkdgXeJzMHt7e7lLXGMIiIiIiLSdI3kCBOB/kHZZ8AN7r6uTttfAWnu/pNaZUOBKe4+yMy+AdwNrAUWAe3c/cogcXyKyCy/fwLXuXs2hyDukrswmFkqkSVRq8xsNPBXdz8u7LhERERERCQcZtYGKHN3N7NxwHh3v+BQ9pnUPKHJfvQEnguGYiuAa0OOR0REREREwnU88H/BlNAdRFbsPyQauRMREREREYkBcbegioiIiIiISCxSciciIiIiIhIDlNyJiIiIiIjEACV3IiIiIiIiMUDJnYiIiIiISAxQciciIiIiIhID/j9NtzU1eXfC2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f87641cc3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_uc.predicted_mean['2018-08-22'].plot(label='Forecast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = pred_uc.predicted_mean['2018-08-22']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv('22-08-2018.csv', header = None, parse_dates=[[0]], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.columns = ['datetime','load' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.index = d['datetime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = d.drop(columns = ['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual = d.asfreq(freq='30T', method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f88b82ea940>]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f87dbe8ea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(prediction.values, color='red', label='prediction')\n",
    "plt.plot(actual.values, color='black', label='actual')\n",
    "plt.plot(delhi['2018-08-21'].values, color='yellow')\n",
    "plt.plot(delhi['2018-08-20'].values, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_data = pd.read_csv('arima_pred.csv', header=None, names=['datetime', 'load'], index_col=[0], parse_dates=[0], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.041666666666666664"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mpred_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masfreq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Convert TimeSeries to specified frequency.\n",
       "\n",
       "Optionally provide filling method to pad/backfill missing values.\n",
       "\n",
       "Returns the original data conformed to a new index with the specified\n",
       "frequency. ``resample`` is more appropriate if an operation, such as\n",
       "summarization, is necessary to represent the data at the new frequency.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "freq : DateOffset object, or string\n",
       "method : {'backfill'/'bfill', 'pad'/'ffill'}, default None\n",
       "    Method to use for filling holes in reindexed Series (note this\n",
       "    does not fill NaNs that already were present):\n",
       "\n",
       "    * 'pad' / 'ffill': propagate last valid observation forward to next\n",
       "      valid\n",
       "    * 'backfill' / 'bfill': use NEXT valid observation to fill\n",
       "how : {'start', 'end'}, default end\n",
       "    For PeriodIndex only, see PeriodIndex.asfreq\n",
       "normalize : bool, default False\n",
       "    Whether to reset output index to midnight\n",
       "fill_value: scalar, optional\n",
       "    Value to use for missing values, applied during upsampling (note\n",
       "    this does not fill NaNs that already were present).\n",
       "\n",
       "    .. versionadded:: 0.20.0\n",
       "\n",
       "Returns\n",
       "-------\n",
       "converted : type of caller\n",
       "\n",
       "Examples\n",
       "--------\n",
       "\n",
       "Start by creating a series with 4 one minute timestamps.\n",
       "\n",
       ">>> index = pd.date_range('1/1/2000', periods=4, freq='T')\n",
       ">>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)\n",
       ">>> df = pd.DataFrame({'s':series})\n",
       ">>> df\n",
       "                       s\n",
       "2000-01-01 00:00:00    0.0\n",
       "2000-01-01 00:01:00    NaN\n",
       "2000-01-01 00:02:00    2.0\n",
       "2000-01-01 00:03:00    3.0\n",
       "\n",
       "Upsample the series into 30 second bins.\n",
       "\n",
       ">>> df.asfreq(freq='30S')\n",
       "                       s\n",
       "2000-01-01 00:00:00    0.0\n",
       "2000-01-01 00:00:30    NaN\n",
       "2000-01-01 00:01:00    NaN\n",
       "2000-01-01 00:01:30    NaN\n",
       "2000-01-01 00:02:00    2.0\n",
       "2000-01-01 00:02:30    NaN\n",
       "2000-01-01 00:03:00    3.0\n",
       "\n",
       "Upsample again, providing a ``fill value``.\n",
       "\n",
       ">>> df.asfreq(freq='30S', fill_value=9.0)\n",
       "                       s\n",
       "2000-01-01 00:00:00    0.0\n",
       "2000-01-01 00:00:30    9.0\n",
       "2000-01-01 00:01:00    NaN\n",
       "2000-01-01 00:01:30    9.0\n",
       "2000-01-01 00:02:00    2.0\n",
       "2000-01-01 00:02:30    9.0\n",
       "2000-01-01 00:03:00    3.0\n",
       "\n",
       "Upsample again, providing a ``method``.\n",
       "\n",
       ">>> df.asfreq(freq='30S', method='bfill')\n",
       "                       s\n",
       "2000-01-01 00:00:00    0.0\n",
       "2000-01-01 00:00:30    NaN\n",
       "2000-01-01 00:01:00    NaN\n",
       "2000-01-01 00:01:30    2.0\n",
       "2000-01-01 00:02:00    2.0\n",
       "2000-01-01 00:02:30    3.0\n",
       "2000-01-01 00:03:00    3.0\n",
       "\n",
       "See Also\n",
       "--------\n",
       "reindex\n",
       "\n",
       "Notes\n",
       "-----\n",
       "To learn more about the frequency strings, please see `this link\n",
       "<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/core/generic.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_data.asfreq?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_data = pred_data.asfreq(freq='5Min', method='bfill')\n",
    "pred_data.to_csv('arima_pred.csv', index_label='datetime', header=['load'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('monthdata.csv', header=None, names=['datetime', 'load'], index_col=[0], parse_dates=[0], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-28 00:00:00</th>\n",
       "      <td>3742.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 01:00:00</th>\n",
       "      <td>3496.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 02:00:00</th>\n",
       "      <td>3329.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 03:00:00</th>\n",
       "      <td>3197.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 04:00:00</th>\n",
       "      <td>3101.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-09-28 00:00:00  3742.89\n",
       "2018-09-28 01:00:00  3496.01\n",
       "2018-09-28 02:00:00  3329.21\n",
       "2018-09-28 03:00:00  3197.15\n",
       "2018-09-28 04:00:00  3101.24"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Read CSV (comma-separated) file into DataFrame\n",
       "\n",
       "Also supports optionally iterating or breaking of the file\n",
       "into chunks.\n",
       "\n",
       "Additional help can be found in the `online docs for IO Tools\n",
       "<http://pandas.pydata.org/pandas-docs/stable/io.html>`_.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any \\\n",
       "object with a read() method (such as a file handle or StringIO)\n",
       "    The string could be a URL. Valid URL schemes include http, ftp, s3, and\n",
       "    file. For file URLs, a host is expected. For instance, a local file could\n",
       "    be file://localhost/path/to/table.csv\n",
       "sep : str, default ','\n",
       "    Delimiter to use. If sep is None, the C engine cannot automatically detect\n",
       "    the separator, but the Python parsing engine can, meaning the latter will\n",
       "    be used and automatically detect the separator by Python's builtin sniffer\n",
       "    tool, ``csv.Sniffer``. In addition, separators longer than 1 character and\n",
       "    different from ``'\\s+'`` will be interpreted as regular expressions and\n",
       "    will also force the use of the Python parsing engine. Note that regex\n",
       "    delimiters are prone to ignoring quoted data. Regex example: ``'\\r\\t'``\n",
       "delimiter : str, default ``None``\n",
       "    Alternative argument name for sep.\n",
       "delim_whitespace : boolean, default False\n",
       "    Specifies whether or not whitespace (e.g. ``' '`` or ``'\\t'``) will be\n",
       "    used as the sep. Equivalent to setting ``sep='\\s+'``. If this option\n",
       "    is set to True, nothing should be passed in for the ``delimiter``\n",
       "    parameter.\n",
       "\n",
       "    .. versionadded:: 0.18.1 support for the Python parser.\n",
       "\n",
       "header : int or list of ints, default 'infer'\n",
       "    Row number(s) to use as the column names, and the start of the\n",
       "    data.  Default behavior is to infer the column names: if no names\n",
       "    are passed the behavior is identical to ``header=0`` and column\n",
       "    names are inferred from the first line of the file, if column\n",
       "    names are passed explicitly then the behavior is identical to\n",
       "    ``header=None``. Explicitly pass ``header=0`` to be able to\n",
       "    replace existing names. The header can be a list of integers that\n",
       "    specify row locations for a multi-index on the columns\n",
       "    e.g. [0,1,3]. Intervening rows that are not specified will be\n",
       "    skipped (e.g. 2 in this example is skipped). Note that this\n",
       "    parameter ignores commented lines and empty lines if\n",
       "    ``skip_blank_lines=True``, so header=0 denotes the first line of\n",
       "    data rather than the first line of the file.\n",
       "names : array-like, default None\n",
       "    List of column names to use. If file contains no header row, then you\n",
       "    should explicitly pass header=None. Duplicates in this list will cause\n",
       "    a ``UserWarning`` to be issued.\n",
       "index_col : int or sequence or False, default None\n",
       "    Column to use as the row labels of the DataFrame. If a sequence is given, a\n",
       "    MultiIndex is used. If you have a malformed file with delimiters at the end\n",
       "    of each line, you might consider index_col=False to force pandas to _not_\n",
       "    use the first column as the index (row names)\n",
       "usecols : list-like or callable, default None\n",
       "    Return a subset of the columns. If list-like, all elements must either\n",
       "    be positional (i.e. integer indices into the document columns) or strings\n",
       "    that correspond to column names provided either by the user in `names` or\n",
       "    inferred from the document header row(s). For example, a valid list-like\n",
       "    `usecols` parameter would be [0, 1, 2] or ['foo', 'bar', 'baz']. Element\n",
       "    order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.\n",
       "    To instantiate a DataFrame from ``data`` with element order preserved use\n",
       "    ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns\n",
       "    in ``['foo', 'bar']`` order or\n",
       "    ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``\n",
       "    for ``['bar', 'foo']`` order.\n",
       "\n",
       "    If callable, the callable function will be evaluated against the column\n",
       "    names, returning names where the callable function evaluates to True. An\n",
       "    example of a valid callable argument would be ``lambda x: x.upper() in\n",
       "    ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster\n",
       "    parsing time and lower memory usage.\n",
       "squeeze : boolean, default False\n",
       "    If the parsed data only contains one column then return a Series\n",
       "prefix : str, default None\n",
       "    Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n",
       "mangle_dupe_cols : boolean, default True\n",
       "    Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n",
       "    'X'...'X'. Passing in False will cause data to be overwritten if there\n",
       "    are duplicate names in the columns.\n",
       "dtype : Type name or dict of column -> type, default None\n",
       "    Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}\n",
       "    Use `str` or `object` together with suitable `na_values` settings\n",
       "    to preserve and not interpret dtype.\n",
       "    If converters are specified, they will be applied INSTEAD\n",
       "    of dtype conversion.\n",
       "engine : {'c', 'python'}, optional\n",
       "    Parser engine to use. The C engine is faster while the python engine is\n",
       "    currently more feature-complete.\n",
       "converters : dict, default None\n",
       "    Dict of functions for converting values in certain columns. Keys can either\n",
       "    be integers or column labels\n",
       "true_values : list, default None\n",
       "    Values to consider as True\n",
       "false_values : list, default None\n",
       "    Values to consider as False\n",
       "skipinitialspace : boolean, default False\n",
       "    Skip spaces after delimiter.\n",
       "skiprows : list-like or integer or callable, default None\n",
       "    Line numbers to skip (0-indexed) or number of lines to skip (int)\n",
       "    at the start of the file.\n",
       "\n",
       "    If callable, the callable function will be evaluated against the row\n",
       "    indices, returning True if the row should be skipped and False otherwise.\n",
       "    An example of a valid callable argument would be ``lambda x: x in [0, 2]``.\n",
       "skipfooter : int, default 0\n",
       "    Number of lines at bottom of file to skip (Unsupported with engine='c')\n",
       "nrows : int, default None\n",
       "    Number of rows of file to read. Useful for reading pieces of large files\n",
       "na_values : scalar, str, list-like, or dict, default None\n",
       "    Additional strings to recognize as NA/NaN. If dict passed, specific\n",
       "    per-column NA values.  By default the following values are interpreted as\n",
       "    NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n",
       "    '1.#IND', '1.#QNAN', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan',\n",
       "    'null'.\n",
       "keep_default_na : bool, default True\n",
       "    Whether or not to include the default NaN values when parsing the data.\n",
       "    Depending on whether `na_values` is passed in, the behavior is as follows:\n",
       "\n",
       "    * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n",
       "      is appended to the default NaN values used for parsing.\n",
       "    * If `keep_default_na` is True, and `na_values` are not specified, only\n",
       "      the default NaN values are used for parsing.\n",
       "    * If `keep_default_na` is False, and `na_values` are specified, only\n",
       "      the NaN values specified `na_values` are used for parsing.\n",
       "    * If `keep_default_na` is False, and `na_values` are not specified, no\n",
       "      strings will be parsed as NaN.\n",
       "\n",
       "    Note that if `na_filter` is passed in as False, the `keep_default_na` and\n",
       "    `na_values` parameters will be ignored.\n",
       "na_filter : boolean, default True\n",
       "    Detect missing value markers (empty strings and the value of na_values). In\n",
       "    data without any NAs, passing na_filter=False can improve the performance\n",
       "    of reading a large file\n",
       "verbose : boolean, default False\n",
       "    Indicate number of NA values placed in non-numeric columns\n",
       "skip_blank_lines : boolean, default True\n",
       "    If True, skip over blank lines rather than interpreting as NaN values\n",
       "parse_dates : boolean or list of ints or names or list of lists or dict, default False\n",
       "\n",
       "    * boolean. If True -> try parsing the index.\n",
       "    * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n",
       "      each as a separate date column.\n",
       "    * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as\n",
       "      a single date column.\n",
       "    * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result\n",
       "      'foo'\n",
       "\n",
       "    If a column or index contains an unparseable date, the entire column or\n",
       "    index will be returned unaltered as an object data type. For non-standard\n",
       "    datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``\n",
       "\n",
       "    Note: A fast-path exists for iso8601-formatted dates.\n",
       "infer_datetime_format : boolean, default False\n",
       "    If True and `parse_dates` is enabled, pandas will attempt to infer the\n",
       "    format of the datetime strings in the columns, and if it can be inferred,\n",
       "    switch to a faster method of parsing them. In some cases this can increase\n",
       "    the parsing speed by 5-10x.\n",
       "keep_date_col : boolean, default False\n",
       "    If True and `parse_dates` specifies combining multiple columns then\n",
       "    keep the original columns.\n",
       "date_parser : function, default None\n",
       "    Function to use for converting a sequence of string columns to an array of\n",
       "    datetime instances. The default uses ``dateutil.parser.parser`` to do the\n",
       "    conversion. Pandas will try to call `date_parser` in three different ways,\n",
       "    advancing to the next if an exception occurs: 1) Pass one or more arrays\n",
       "    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n",
       "    string values from the columns defined by `parse_dates` into a single array\n",
       "    and pass that; and 3) call `date_parser` once for each row using one or\n",
       "    more strings (corresponding to the columns defined by `parse_dates`) as\n",
       "    arguments.\n",
       "dayfirst : boolean, default False\n",
       "    DD/MM format dates, international and European format\n",
       "iterator : boolean, default False\n",
       "    Return TextFileReader object for iteration or getting chunks with\n",
       "    ``get_chunk()``.\n",
       "chunksize : int, default None\n",
       "    Return TextFileReader object for iteration.\n",
       "    See the `IO Tools docs\n",
       "    <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_\n",
       "    for more information on ``iterator`` and ``chunksize``.\n",
       "compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'\n",
       "    For on-the-fly decompression of on-disk data. If 'infer' and\n",
       "    `filepath_or_buffer` is path-like, then detect compression from the\n",
       "    following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no\n",
       "    decompression). If using 'zip', the ZIP file must contain only one data\n",
       "    file to be read in. Set to None for no decompression.\n",
       "\n",
       "    .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.\n",
       "\n",
       "thousands : str, default None\n",
       "    Thousands separator\n",
       "decimal : str, default '.'\n",
       "    Character to recognize as decimal point (e.g. use ',' for European data).\n",
       "float_precision : string, default None\n",
       "    Specifies which converter the C engine should use for floating-point\n",
       "    values. The options are `None` for the ordinary converter,\n",
       "    `high` for the high-precision converter, and `round_trip` for the\n",
       "    round-trip converter.\n",
       "lineterminator : str (length 1), default None\n",
       "    Character to break file into lines. Only valid with C parser.\n",
       "quotechar : str (length 1), optional\n",
       "    The character used to denote the start and end of a quoted item. Quoted\n",
       "    items can include the delimiter and it will be ignored.\n",
       "quoting : int or csv.QUOTE_* instance, default 0\n",
       "    Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n",
       "    QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n",
       "doublequote : boolean, default ``True``\n",
       "   When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate\n",
       "   whether or not to interpret two consecutive quotechar elements INSIDE a\n",
       "   field as a single ``quotechar`` element.\n",
       "escapechar : str (length 1), default None\n",
       "    One-character string used to escape delimiter when quoting is QUOTE_NONE.\n",
       "comment : str, default None\n",
       "    Indicates remainder of line should not be parsed. If found at the beginning\n",
       "    of a line, the line will be ignored altogether. This parameter must be a\n",
       "    single character. Like empty lines (as long as ``skip_blank_lines=True``),\n",
       "    fully commented lines are ignored by the parameter `header` but not by\n",
       "    `skiprows`. For example, if ``comment='#'``, parsing\n",
       "    ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being\n",
       "    treated as the header.\n",
       "encoding : str, default None\n",
       "    Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n",
       "    standard encodings\n",
       "    <https://docs.python.org/3/library/codecs.html#standard-encodings>`_\n",
       "dialect : str or csv.Dialect instance, default None\n",
       "    If provided, this parameter will override values (default or not) for the\n",
       "    following parameters: `delimiter`, `doublequote`, `escapechar`,\n",
       "    `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to\n",
       "    override values, a ParserWarning will be issued. See csv.Dialect\n",
       "    documentation for more details.\n",
       "tupleize_cols : boolean, default False\n",
       "    .. deprecated:: 0.21.0\n",
       "       This argument will be removed and will always convert to MultiIndex\n",
       "\n",
       "    Leave a list of tuples on columns as is (default is to convert to\n",
       "    a MultiIndex on the columns)\n",
       "error_bad_lines : boolean, default True\n",
       "    Lines with too many fields (e.g. a csv line with too many commas) will by\n",
       "    default cause an exception to be raised, and no DataFrame will be returned.\n",
       "    If False, then these \"bad lines\" will dropped from the DataFrame that is\n",
       "    returned.\n",
       "warn_bad_lines : boolean, default True\n",
       "    If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n",
       "    \"bad line\" will be output.\n",
       "low_memory : boolean, default True\n",
       "    Internally process the file in chunks, resulting in lower memory use\n",
       "    while parsing, but possibly mixed type inference.  To ensure no mixed\n",
       "    types either set False, or specify the type with the `dtype` parameter.\n",
       "    Note that the entire file is read into a single DataFrame regardless,\n",
       "    use the `chunksize` or `iterator` parameter to return the data in chunks.\n",
       "    (Only valid with C parser)\n",
       "memory_map : boolean, default False\n",
       "    If a filepath is provided for `filepath_or_buffer`, map the file object\n",
       "    directly onto memory and access the data directly from there. Using this\n",
       "    option can improve performance because there is no longer any I/O overhead.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "result : DataFrame or TextParser\n",
       "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/envs/ML/lib/python3.7/site-packages/pandas/io/parsers.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28/09/2018 00:00,3742.890\n",
      "28/09/2018 00:05,3688.820\n",
      "28/09/2018 00:10,3679.720\n",
      "28/09/2018 00:15,3664.890\n",
      "28/09/2018 00:20,3638.680\n",
      "28/09/2018 00:25,3616.820\n",
      "28/09/2018 00:30,3601.250\n",
      "28/09/2018 00:35,3566.840\n",
      "28/09/2018 00:40,3568.250\n",
      "28/09/2018 00:45,3547.410\n"
     ]
    }
   ],
   "source": [
    "!head monthdata.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the month long data\n",
    "data = pd.read_csv('monthdata.csv', header=None, index_col=['datetime'], names=['datetime', 'load'], parse_dates=['datetime'], infer_datetime_format=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:00:00</th>\n",
       "      <td>3942.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:05:00</th>\n",
       "      <td>3911.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:10:00</th>\n",
       "      <td>3923.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:15:00</th>\n",
       "      <td>3877.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:20:00</th>\n",
       "      <td>3833.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-09-29 00:00:00  3942.27\n",
       "2018-09-29 00:05:00  3911.38\n",
       "2018-09-29 00:10:00  3923.22\n",
       "2018-09-29 00:15:00  3877.00\n",
       "2018-09-29 00:20:00  3833.22"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{datetime.date(2018, 9, 29),\n",
       " datetime.date(2018, 9, 30),\n",
       " datetime.date(2018, 10, 1),\n",
       " datetime.date(2018, 10, 2),\n",
       " datetime.date(2018, 10, 3),\n",
       " datetime.date(2018, 10, 4),\n",
       " datetime.date(2018, 10, 5),\n",
       " datetime.date(2018, 10, 6),\n",
       " datetime.date(2018, 10, 7),\n",
       " datetime.date(2018, 10, 8),\n",
       " datetime.date(2018, 10, 9),\n",
       " datetime.date(2018, 10, 10),\n",
       " datetime.date(2018, 10, 11),\n",
       " datetime.date(2018, 10, 12),\n",
       " datetime.date(2018, 10, 13),\n",
       " datetime.date(2018, 10, 14),\n",
       " datetime.date(2018, 10, 15),\n",
       " datetime.date(2018, 10, 16),\n",
       " datetime.date(2018, 10, 17),\n",
       " datetime.date(2018, 10, 18),\n",
       " datetime.date(2018, 10, 19),\n",
       " datetime.date(2018, 10, 20),\n",
       " datetime.date(2018, 10, 21),\n",
       " datetime.date(2018, 10, 22),\n",
       " datetime.date(2018, 10, 23),\n",
       " datetime.date(2018, 10, 24),\n",
       " datetime.date(2018, 10, 25),\n",
       " datetime.date(2018, 10, 26),\n",
       " datetime.date(2018, 10, 27)}"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(data.index.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:35:00</th>\n",
       "      <td>2402.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:40:00</th>\n",
       "      <td>2371.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:45:00</th>\n",
       "      <td>2348.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:50:00</th>\n",
       "      <td>2336.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-27 23:55:00</th>\n",
       "      <td>2316.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-10-27 23:35:00  2402.19\n",
       "2018-10-27 23:40:00  2371.37\n",
       "2018-10-27 23:45:00  2348.32\n",
       "2018-10-27 23:50:00  2336.29\n",
       "2018-10-27 23:55:00  2316.86"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:'27/10/2018'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[:'27/10/2018'].to_csv('monthdata.csv', header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('monthdata.csv', header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-27 23:10:00,2520.48\n",
      "2018-10-27 23:15:00,2490.5\n",
      "2018-10-27 23:20:00,2478.54\n",
      "2018-10-27 23:25:00,2433.26\n",
      "2018-10-27 23:30:00,2422.02\n",
      "2018-10-27 23:35:00,2402.19\n",
      "2018-10-27 23:40:00,2371.37\n",
      "2018-10-27 23:45:00,2348.32\n",
      "2018-10-27 23:50:00,2336.29\n",
      "2018-10-27 23:55:00,2316.86\n"
     ]
    }
   ],
   "source": [
    "!tail monthdata.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-09-28 00:00:00', '2018-09-28 00:05:00',\n",
       "               '2018-09-28 00:10:00', '2018-09-28 00:15:00',\n",
       "               '2018-09-28 00:20:00', '2018-09-28 00:25:00',\n",
       "               '2018-09-28 00:30:00', '2018-09-28 00:35:00',\n",
       "               '2018-09-28 00:40:00', '2018-09-28 00:45:00',\n",
       "               ...\n",
       "               '2018-10-28 23:10:00', '2018-10-28 23:15:00',\n",
       "               '2018-10-28 23:20:00', '2018-10-28 23:25:00',\n",
       "               '2018-10-28 23:30:00', '2018-10-28 23:35:00',\n",
       "               '2018-10-28 23:40:00', '2018-10-28 23:45:00',\n",
       "               '2018-10-28 23:50:00', '2018-10-28 23:55:00'],\n",
       "              dtype='datetime64[ns]', name='datetime', length=8916, freq=None)"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-28 00:00:00</th>\n",
       "      <td>3742.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 00:05:00</th>\n",
       "      <td>3688.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 00:10:00</th>\n",
       "      <td>3679.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 00:15:00</th>\n",
       "      <td>3664.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28 00:20:00</th>\n",
       "      <td>3638.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-09-28 00:00:00  3742.89\n",
       "2018-09-28 00:05:00  3688.82\n",
       "2018-09-28 00:10:00  3679.72\n",
       "2018-09-28 00:15:00  3664.89\n",
       "2018-09-28 00:20:00  3638.68"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['28/09/2018'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('monthdata.csv', header=None, index_col=['datetime'], names=['datetime', 'load'], parse_dates=['datetime'], infer_datetime_format=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8628, 1)"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.asfreq(freq='15Min', method='bfill')  # sample the data in hourly manner\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:00:00</th>\n",
       "      <td>3942.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:15:00</th>\n",
       "      <td>3877.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:30:00</th>\n",
       "      <td>3816.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 00:45:00</th>\n",
       "      <td>3770.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-29 01:00:00</th>\n",
       "      <td>3735.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-09-29 00:00:00  3942.27\n",
       "2018-09-29 00:15:00  3877.00\n",
       "2018-09-29 00:30:00  3816.82\n",
       "2018-09-29 00:45:00  3770.52\n",
       "2018-09-29 01:00:00  3735.92"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook delhi.ipynb to script\n",
      "[NbConvertApp] Writing 73498 bytes to delhi.py\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert --to script delhi.ipynb"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ML",
   "language": "python",
   "name": "ml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
